Input: The construction of causal graphs from non-experimental data rests on a set of constraints that the graph structure imposes on all probability distributions compatible with the graph. These constraints are of two types: conditional inde-pendencies and algebraic constraints, first noted by Verma. While conditional independencies are well studied and frequently used in causal induction algorithms, Verma constraints are still poorly understood, and rarely applied. In this paper we examine a special subset of Verma constraints which are easy to understand, easy to identify and easy to apply; they arise from'' dormant independencies,'' namely, conditional independencies that hold in interventional distributions. We give a complete algorithm for determining if a dormant independence between two sets of variables is entailed by the causal graph, such that this independence is identifiable, in other words if it resides in an interventional distribution that can be predicted without resorting to interventions. We further show the usefulness of dormant independencies in model testing and induction by giving an algorithm that uses constraints entailed by dormant independencies to prune extraneous edges from a given causal graph.

construction of causal graphs

model testing

induction

non-experimental data

constraints

graph structure

probability distributions

graph

conditional inde-pendencies

algebraic constraints

conditional independencies

Verma constraints

Verma constraints

dormant independencies

conditional independencies

interventional distributions

dormant independence

variables

causal graph

independence

interventional distribution

interventions

dormant independencies

dormant independencies

extraneous edges

causal graph

constraints

they

algorithm

it

algorithm

constraints

causal induction algorithms

non-experimental data

construction of causal graphs

conditional independencies

causal induction algorithms

algorithm

dormant independence

dormant independencies

model testing

dormant independencies

induction

algorithm

extraneous edges

constraints

algorithm

probability distributions

graph

conditional independencies

interventional distributions

interventional distribution

it

conditional inde-pendencies

constraints

algebraic constraints

constraints

Verma constraints

conditional independencies

dormant independencies

conditional independencies

model testing

induction

extraneous edges

causal graph


Input: With the recent popularity of animated GIFs on social media, there is need for ways to index them with rich meta-data. To advance research on animated GIF understanding, we collected a new dataset, Tumblr GIF -LRB- TGIF -RRB-, with 100K animated GIFs from Tumblr and 120K natural language descriptions obtained via crowdsourcing. The motivation for this work is to develop a testbed for image sequence description systems, where the task is to generate natural language descriptions for animated GIFs or video clips. To ensure a high quality dataset, we developed a series of novel quality controls to validate free-form text input from crowd-workers. We show that there is unambiguous association between visual content and natural language descriptions in our dataset, making it an ideal benchmark for the visual content captioning task. We perform extensive statistical analyses to compare our dataset to existing image and video description datasets. Next, we provide baseline results on the animated GIF description task, using three representative techniques: nearest neighbor, statistical machine translation, and recurrent neural networks. Finally, we show that models fine-tuned from our animated GIF description dataset can be helpful for automatic movie description.

animated GIFs

social media

rich meta-data

Tumblr GIF -LRB- TGIF -RRB-

animated GIFs

natural language descriptions

natural language descriptions

animated GIFs

video clips

free-form text input

visual content

natural language descriptions

image and video description datasets

animated GIF description dataset

animated GIF understanding

image sequence description systems

visual content captioning task

animated GIF description task

automatic movie description

dataset

dataset

it

dataset

representative techniques

crowdsourcing

quality controls

nearest neighbor

statistical machine translation

recurrent neural networks

statistical analyses

social media

animated GIFs

dataset

animated GIF understanding

crowdsourcing

natural language descriptions

natural language descriptions

animated GIFs

natural language descriptions

video clips

quality controls

free-form text input

representative techniques

animated GIF description task

animated GIF description dataset

automatic movie description

natural language descriptions

animated GIFs

animated GIFs

video clips

visual content

natural language descriptions

nearest neighbor

statistical machine translation

statistical machine translation

recurrent neural networks

visual content

dataset

natural language descriptions

dataset

it

visual content captioning task

dataset

image and video description datasets

nearest neighbor

representative techniques

statistical machine translation

representative techniques

recurrent neural networks

representative techniques


Input: Systemic grammar has been used for AI text generation work in the past, but the implementations have tended be ad hoc or inefficient. This paper presents an approach to systemic text generation where AI problem solving techniques are applied directly to an unadulterated systemic grammar. This approach is made possible by a special relationship between systemic grammar and problem solving: both are organized primarily as choosing from alternatives. The result is simple, efficient text generation firmly based in a linguistic theory.

Systemic grammar

AI problem solving techniques

systemic grammar

systemic grammar

problem solving

linguistic theory

AI text generation

text generation

text generation

implementations

approach

approach

Systemic grammar

AI text generation

approach

text generation

AI problem solving techniques

systemic grammar

linguistic theory

text generation

systemic grammar

problem solving


Input: In this paper a novel solution to automatic and unsupervised word sense induction -LRB- WSI -RRB- is introduced. It represents an instantiation of the one sense per collocation observation -LRB- Gale et al., 1992 -RRB-. Like most existing approaches it utilizes clustering of word co-occurrences. This approach differs from other approaches to WSI in that it enhances the effect of the one sense per collocation observation by using triplets of words instead of pairs. The combination with a two-step clustering process using sentence co-occurrences as features allows for accurate results. Additionally, a novel and likewise automatic and unsupervised evaluation method inspired by Schutze's -LRB- 1992 -RRB- idea of evaluation of word sense disambiguation algorithms is employed. Offering advantages like reproducability and independency of a given biased gold standard it also enables automatic parameter optimization of the WSI algorithm.

solution

It

it

approach

approaches

it

automatic and unsupervised word sense induction -LRB- WSI -RRB-

WSI

one sense per collocation observation

clustering of word co-occurrences

one sense per collocation observation

two-step clustering process

word sense disambiguation algorithms

automatic parameter optimization

WSI algorithm

triplets of words

sentence co-occurrences

features

automatic and unsupervised evaluation method

solution

automatic and unsupervised word sense induction -LRB- WSI -RRB-

clustering of word co-occurrences

it

approach

WSI

approaches

WSI

it

one sense per collocation observation

triplets of words

it

sentence co-occurrences

two-step clustering process

automatic parameter optimization

WSI algorithm

It

one sense per collocation observation

approach

approaches

automatic and unsupervised evaluation method

word sense disambiguation algorithms


Input: This abstract describes a natural language system which deals usefully with ungrammatical input and describes some actual and potential applications of it in computer aided second language learning. However, this is not the only area in which the principles of the system might be used, and the aim in building it was simply to demonstrate the workability of the general mechanism, and provide a framework for assessing developments of it.

natural language system

ungrammatical input

it

this

system

it

it

computer aided second language learning

natural language system

ungrammatical input

it

computer aided second language learning

system

this


Input: In a motorized vehicle a number of easily measurable signals with frequency components related to the rotational speed of the engine can be found, e.g., vibrations, electrical system voltage level, and ambient sound. These signals could potentially be used to estimate the speed and related states of the vehicle. Unfortunately, such estimates would typically require the relations -LRB- scale factors -RRB- between the frequency components and the speed for different gears to be known. Consequently, in this article we look at the problem of estimating these gear scale factors from training data consisting only of speed measurements and measurements of the signal in question. The estimation problem is formulated as a maximum likelihood estimation problem and heuristics is used to find initial values for a numerical evaluation of the estimator. Finally, a measurement campaign is conducted and the functionality of the estimation method is verified on real data.

measurable signals

real data

frequency components

frequency components

heuristics

estimation method

rotational speed of the engine

vibrations

electrical system voltage level

ambient sound

speed

gears

gear scale factors

speed measurements

signals

training data

estimator

speed and related states of the vehicle

estimation problem

maximum likelihood estimation problem

numerical evaluation of the estimator

frequency components

measurable signals

signals

speed and related states of the vehicle

training data

gear scale factors

maximum likelihood estimation problem

estimation problem

heuristics

numerical evaluation of the estimator

frequency components

rotational speed of the engine

speed

gears

vibrations

measurable signals

electrical system voltage level

measurable signals

ambient sound

measurable signals

vibrations

electrical system voltage level

electrical system voltage level

ambient sound

frequency components

speed

real data

estimation method


Input: LPC based speech coders operating at bit rates below 3.0 kbits/sec are usually associated with buzzy or metallic artefacts in the synthetic speech. These are mainly attributable to the simplifying assumptions made about the excitation source, which are usually required to maintain such low bit rates. In this paper a new LPC vocoder is presented which splits the LPC excitation into two frequency bands using a variable cutoff frequency. The lower band is responsible for representing the voiced parts of speech, whilst the upper band represents unvoiced speech. In doing so the coder's performance during both mixed voicing speech and speech containing acoustic noise is greatly improved, producing soft natural sounding speech. The paper also describes new parameter determination and quantisation techniques vital to the operation of this coder at such low bit rates.

LPC based speech coders

LPC vocoder

parameter determination

quantisation techniques

bit rates

buzzy or metallic artefacts

excitation source

low bit rates

LPC excitation

frequency bands

variable cutoff frequency

low bit rates

synthetic speech

voiced parts of speech

unvoiced speech

mixed voicing speech

speech containing acoustic noise

soft natural sounding speech

coder

coder

bit rates

LPC based speech coders

buzzy or metallic artefacts

synthetic speech

low bit rates

coder

variable cutoff frequency

LPC excitation

variable cutoff frequency

frequency bands

coder

soft natural sounding speech

mixed voicing speech

coder

speech containing acoustic noise

coder

parameter determination

coder

quantisation techniques

coder

parameter determination

quantisation techniques


Input: We consider a problem of blind source separation from a set of instantaneous linear mixtures, where the mixing matrix is unknown. It was discovered recently, that exploiting the sparsity of sources in an appropriate representation according to some signal dictionary, dramatically improves the quality of separation. In this work we use the property of multi scale transforms, such as wavelet or wavelet packets, to decompose signals into sets of local features with various degrees of sparsity. We use this intrinsic property for selecting the best -LRB- most sparse -RRB- subsets of features for further separation. The performance of the algorithm is verified on noise-free and noisy data. Experiments with simulated signals, musical sounds and images demonstrate significant improvement of separation quality over previously reported results.

blind source separation

instantaneous linear mixtures

mixing matrix

sparsity of sources

signal dictionary

multi scale transforms

wavelet or wavelet packets

local features

sparsity

features

quality of separation

separation quality

algorithm

noise-free and noisy data

simulated signals

musical sounds

images

instantaneous linear mixtures

blind source separation

signal dictionary

sparsity of sources

quality of separation

sparsity of sources

noise-free and noisy data

algorithm

simulated signals

separation quality

musical sounds

separation quality

images

separation quality

wavelet or wavelet packets

multi scale transforms

simulated signals

musical sounds

musical sounds

images


Input: In this paper, we explore multilingual feature-level data sharing via Deep Neural Network -LRB- DNN -RRB- stacked bottleneck features. Given a set of available source languages, we apply language identification to pick the language most similar to the target language, for more efficient use of multilingual resources. Our experiments with IARPA-Babel languages show that bottleneck features trained on the most similar source language perform better than those trained on all available source languages. Further analysis suggests that only data similar to the target language is useful for multilingual training.

multilingual feature-level data sharing

language identification

multilingual training

Deep Neural Network -LRB- DNN -RRB- stacked bottleneck features

multilingual resources

IARPA-Babel languages

bottleneck features

those

data

Deep Neural Network -LRB- DNN -RRB- stacked bottleneck features

multilingual feature-level data sharing

language identification

multilingual resources

data

multilingual training

those

bottleneck features


Input: This article introduces a bidirectional grammar generation system called feature structure-directed generation, developed for a dialogue translation system. The system utilizes typed feature structures to control the top-down derivation in a declarative way. This generation system also uses disjunctive feature structures to reduce the number of copies of the derivation tree. The grammar for this generator is designed to properly generate the speaker's intention in a telephone dialogue.

bidirectional grammar generation system

feature structure-directed generation

generation system

grammar

dialogue translation system

system

generator

typed feature structures

top-down derivation

disjunctive feature structures

derivation tree

speaker's intention

telephone dialogue

bidirectional grammar generation system

dialogue translation system

feature structure-directed generation

dialogue translation system

typed feature structures

system

typed feature structures

top-down derivation

disjunctive feature structures

generation system

disjunctive feature structures

derivation tree

grammar

generator

grammar

speaker's intention

feature structure-directed generation

bidirectional grammar generation system

telephone dialogue

speaker's intention


Input: Automatic image annotation is a newly developed and promising technique to provide semantic image retrieval via text descriptions. It concerns a process of automatically labeling the image contents with a pre-defined set of keywords which are exploited to represent the image semantics. A Maximum Entropy Model-based approach to the task of automatic image annotation is proposed in this paper. In the phase of training, a basic visual vocabulary consisting of blob-tokens to describe the image content is generated at first; then the statistical relationship is modeled between the blob-tokens and keywords by a Maximum Entropy Model constructed from the training set of labeled images. In the phase of annotation, for an unlabeled image, the most likely associated keywords are predicted in terms of the blob-token set extracted from the given image. We carried out experiments on a medium-sized image collection with about 5000 images from Corel Photo CDs. The experimental results demonstrated that the annotation performance of this method outperforms some traditional annotation methods by about 8% in mean precision, showing a potential of the Maximum Entropy Model in the task of automatic image annotation.

Automatic image annotation

semantic image retrieval

automatically labeling the image contents

automatic image annotation

training

annotation

annotation

automatic image annotation

text descriptions

medium-sized image collection

Corel Photo CDs

keywords

image semantics

visual vocabulary

blob-tokens

image content

blob-tokens

keywords

keywords

blob-token set

Maximum Entropy Model-based approach

Maximum Entropy Model

annotation methods

Maximum Entropy Model

statistical relationship

method

mean precision

Automatic image annotation

semantic image retrieval

text descriptions

semantic image retrieval

keywords

automatically labeling the image contents

keywords

image semantics

Maximum Entropy Model-based approach

automatic image annotation

visual vocabulary

image content

Maximum Entropy Model

statistical relationship

blob-token set

keywords

Corel Photo CDs

medium-sized image collection

method

annotation

annotation methods

annotation

Maximum Entropy Model

automatic image annotation

blob-tokens

visual vocabulary

method

annotation methods

mean precision

annotation methods


Input: Recent years have seen increasing research on extracting and using temporal information in natural language applications. However most of the works found in the literature have focused on identifying and understanding temporal expressions in newswire texts. In this paper we report our work on anchoring temporal expressions in a novel genre, emails. The highly under-specified nature of these expressions fits well with our constraint-based representation of time, Time Calculus for Natural Language -LRB- TCNL -RRB-. We have developed and evaluated a Temporal Expression Anchoror -LRB- TEA -RRB-, and the result shows that it performs significantly better than the baseline, and compares favorably with some of the closely related work.

temporal information

temporal expressions

temporal expressions in newswire texts

temporal expressions

constraint-based representation of time

Time Calculus for Natural Language -LRB- TCNL -RRB-

natural language applications

newswire texts

emails

expressions

it

baseline

Temporal Expression Anchoror -LRB- TEA -RRB-

newswire texts

temporal expressions

emails

temporal expressions

Time Calculus for Natural Language -LRB- TCNL -RRB-

constraint-based representation of time

it

baseline


Input: We address the problem of populating object category detection datasets with dense, per-object 3D reconstructions, bootstrapped from class labels, ground truth figure-ground segmentations and a small set of keypoint annotations. Our proposed algorithm first estimates camera viewpoint using rigid structure-from-motion, then reconstructs object shapes by optimizing over visual hull proposals guided by loose within-class shape similarity assumptions. The visual hull sampling process attempts to intersect an object's projection cone with the cones of minimal subsets of other similar objects among those pictured from certain vantage points. We show that our method is able to produce convincing per-object 3D reconstructions on one of the most challenging existing object-category detection datasets, PASCAL VOC. Our results may re-stimulate once popular geometry-oriented model-based recognition approaches.

object category detection datasets

object-category detection datasets

PASCAL VOC

per-object 3D reconstructions

per-object 3D reconstructions

ground truth figure-ground segmentations

keypoint annotations

visual hull sampling process

geometry-oriented model-based recognition approaches

algorithm

method

camera viewpoint

rigid structure-from-motion

object shapes

visual hull proposals

loose within-class shape similarity assumptions

projection cone

object category detection datasets

per-object 3D reconstructions

ground truth figure-ground segmentations

per-object 3D reconstructions

keypoint annotations

per-object 3D reconstructions

algorithm

camera viewpoint

algorithm

object shapes

rigid structure-from-motion

algorithm

visual hull proposals

object shapes

loose within-class shape similarity assumptions

visual hull proposals

method

per-object 3D reconstructions

object-category detection datasets

method

ground truth figure-ground segmentations

keypoint annotations

PASCAL VOC

object-category detection datasets


Input: Probabilistic models have been previously shown to be efficient and effective for modeling and recognition of human motion. In particular we focus on methods which represent the human motion model as a triangulated graph. Previous approaches learned models based just on positions and velocities of the body parts while ignoring their appearance. Moreover, a heuristic approach was commonly used to obtain translation invariance. In this paper we suggest an improved approach for learning such models and using them for human motion recognition. The suggested approach combines multiple cues, i.e., positions, velocities and appearance into both the learning and detection phases. Furthermore, we introduce global variables in the model, which can represent global properties such as translation, scale or viewpoint. The model is learned in an unsupervised manner from un-labelled data. We show that the suggested hybrid proba-bilistic model -LRB- which combines global variables, like translation, with local variables, like relative positions and appearances of body parts -RRB-, leads to: -LRB- i -RRB- faster convergence of learning phase, -LRB- ii -RRB- robustness to occlusions, and, -LRB- iii -RRB- higher recognition rate.

Probabilistic models

human motion model

triangulated graph

heuristic approach

unsupervised manner

hybrid proba-bilistic model

modeling and recognition of human motion

human motion recognition

learning and detection phases

learning phase

models

approach

models

them

approach

cues

model

model

positions

velocities

appearance

translation invariance

positions

velocities

appearance

global variables

global properties

translation

scale

viewpoint

global variables

translation

local variables

relative positions

appearances of body parts

occlusions

un-labelled data

recognition rate

faster convergence

robustness

Probabilistic models

modeling and recognition of human motion

triangulated graph

human motion model

positions

models

velocities

models

heuristic approach

translation invariance

approach

models

them

human motion recognition

approach

learning and detection phases

global variables

model

global variables

global properties

unsupervised manner

model

un-labelled data

unsupervised manner

global variables

hybrid proba-bilistic model

positions

velocities

positions

velocities

velocities

appearance

translation

scale

scale

viewpoint

relative positions

appearances of body parts

faster convergence

robustness

robustness

recognition rate

positions

cues

velocities

cues

appearance

cues

translation

global properties

scale

global properties

viewpoint

global properties

translation

global variables

relative positions

local variables

appearances of body parts

local variables

faster convergence

learning phase


Input: Factor analysis and principal components analysis can be used to model linear relationships between observed variables and linearly map high-dimensional data to a lower-dimensional hidden space. In factor analysis, the observations are modeled as a linear combination of normally distributed hidden variables. We describe a nonlinear generalization of factor analysis, called ``product analy-sis'', that models the observed variables as a linear combination of products of normally distributed hidden variables. Just as factor analysis can be viewed as unsupervised linear regression on unobserved, normally distributed hidden variables, product analysis can be viewed as unsupervised linear regression on products of unobserved, normally distributed hidden variables. The mapping between the data and the hidden space is nonlinear, so we use an approximate variational technique for inference and learning. Since product analysis is a generalization of factor analysis, product analysis always finds a higher data likelihood than factor analysis. We give results on pattern recognition and illumination-invariant image clustering.

Factor analysis

principal components analysis

factor analysis

linear combination of normally distributed hidden variables

nonlinear generalization of factor analysis

factor analysis

``product analy-sis''

factor analysis

unsupervised linear regression

product analysis

unsupervised linear regression

approximate variational technique

product analysis

generalization of factor analysis

factor analysis

product analysis

factor analysis

linear relationships between observed variables

lower-dimensional hidden space

observed variables

linear combination of products of normally distributed hidden variables

distributed hidden variables

distributed hidden variables

hidden space

high-dimensional data

inference

learning

pattern recognition

illumination-invariant image clustering

Factor analysis

principal components analysis

inference

learning

pattern recognition

illumination-invariant image clustering

Factor analysis

linear relationships between observed variables

principal components analysis

linear relationships between observed variables

nonlinear generalization of factor analysis

observed variables

linear combination of products of normally distributed hidden variables

nonlinear generalization of factor analysis

unsupervised linear regression

factor analysis

unsupervised linear regression

product analysis

approximate variational technique

inference

approximate variational technique

learning

``product analy-sis''

nonlinear generalization of factor analysis

product analysis

generalization of factor analysis

product analysis

factor analysis


Input: This paper describes a domain independent strategy for the multimedia articulation of answers elicited by a natural language interface to database query applications. Multimedia answers include videodisc images and heuristically-produced complete sentences in text or text-to-speech form. Deictic reference and feedback about the discourse are enabled. The interface thus presents the application as cooperative and conversational.

domain independent strategy

multimedia articulation of answers

database query applications

natural language interface

text-to-speech form

Deictic reference

feedback

discourse

Multimedia answers

videodisc images

interface

application

domain independent strategy

multimedia articulation of answers

multimedia articulation of answers

natural language interface

natural language interface

database query applications

videodisc images

Multimedia answers

Deictic reference

feedback

Deictic reference

discourse

feedback

discourse


Input: The LOGON MT demonstrator assembles independently valuable general-purpose NLP components into a machine translation pipeline that capitalizes on output quality. The demonstrator embodies an interesting combination of hand-built, symbolic resources and stochastic processes.

LOGON MT demonstrator

general-purpose NLP components

machine translation pipeline

stochastic processes

demonstrator

hand-built, symbolic resources

LOGON MT demonstrator

general-purpose NLP components

general-purpose NLP components

machine translation pipeline

hand-built, symbolic resources

demonstrator

stochastic processes

demonstrator

hand-built, symbolic resources

stochastic processes


Input: In this paper we investigate the simple logical properties of contexts. We describe both the syntax and semantics of a general propositional language of context, and give a Hilbert style proof system for this language. A propositional logic of context extends classical propositional logic in two ways. Firstly, a new modality, ist -LRB- ;-RRB-, is introduced. It is used to express that the sentence,, holds in the context. Secondly, each context has its own vocabulary, i.e. a set of propositional atoms which are deened or meaningful in that context. The main results of this paper are the sound-ness and completeness of this Hilbert style proof system. We also provide soundness and completeness results -LRB- i.e. correspondence theory -RRB- for various extensions of the general system.

logical properties of contexts

syntax

semantics

propositional language of context

propositional logic of context

classical propositional logic

modality

correspondence theory

Hilbert style proof system

Hilbert style proof system

language

It

syntax

semantics

syntax

propositional language of context

semantics

propositional language of context

Hilbert style proof system

language

classical propositional logic

propositional logic of context


Input: Image matching is a fundamental problem in Computer Vision. In the context of feature-based matching, SIFT and its variants have long excelled in a wide array of applications. However, for ultra-wide baselines, as in the case of aerial images captured under large camera rotations, the appearance variation goes beyond the reach of SIFT and RANSAC. In this paper we propose a data-driven, deep learning-based approach that sidesteps local correspondence by framing the problem as a classification task. Furthermore, we demonstrate that local correspondences can still be useful. To do so we incorporate an attention mechanism to produce a set of probable matches, which allows us to further increase performance. We train our models on a dataset of urban aerial imagery consisting of `same' and `different' pairs, collected for this purpose, and characterize the problem via a human study with annotations from Amazon Mechanical Turk. We demonstrate that our models outperform the state-of-the-art on ultra-wide baseline matching and approach human accuracy.

Image matching

Computer Vision

feature-based matching

classification task

ultra-wide baseline matching

SIFT

SIFT

RANSAC

data-driven, deep learning-based approach

attention mechanism

human study

ultra-wide baselines

large camera rotations

appearance variation

local correspondence

local correspondences

aerial images

dataset of urban aerial imagery

annotations from Amazon Mechanical Turk

problem

models

problem

models

state-of-the-art

human accuracy

Image matching

Computer Vision

SIFT

feature-based matching

classification task

problem

dataset of urban aerial imagery

models

human study

problem

annotations from Amazon Mechanical Turk

human study

large camera rotations

aerial images

SIFT

RANSAC

models

state-of-the-art

models

human accuracy

ultra-wide baseline matching

models

ultra-wide baseline matching

state-of-the-art


Input: This article is devoted to the problem of quantifying noun groups in German. After a thorough description of the phenomena, the results of corpus-based investigations are described. Moreover, some examples are given that underline the necessity of integrating some kind of information other than grammar sensu stricto into the treebank. We argue that a more sophisticated and fine-grained annotation in the tree-bank would have very positve effects on stochastic parsers trained on the tree-bank and on grammars induced from the treebank, and it would make the treebank more valuable as a source of data for theoretical linguistic investigations. The information gained from corpus research and the analyses that are proposed are realized in the framework of SILVA, a parsing and extraction tool for German text corpora.

quantifying noun groups in German

theoretical linguistic investigations

German

treebank

tree-bank

tree-bank

treebank

treebank

German text corpora

grammar sensu stricto

fine-grained annotation

stochastic parsers

grammars

SILVA

parsing and extraction tool

fine-grained annotation

stochastic parsers

tree-bank

stochastic parsers

treebank

grammars

treebank

theoretical linguistic investigations

German text corpora

SILVA

SILVA

parsing and extraction tool


Input: While paraphrasing is critical both for interpretation and generation of natural language, current systems use manual or semi-automatic methods to collect paraphrases. We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source text. Our approach yields phrasal and single word lexical paraphrases as well as syntactic paraphrases.

paraphrasing

manual or semi-automatic methods

unsupervised learning algorithm

interpretation and generation of natural language

identification of paraphrases

systems

approach

paraphrases

phrasal and single word lexical paraphrases

syntactic paraphrases

corpus of multiple English translations

paraphrasing

interpretation and generation of natural language

systems

paraphrases

manual or semi-automatic methods

systems

unsupervised learning algorithm

identification of paraphrases

corpus of multiple English translations

identification of paraphrases

approach

phrasal and single word lexical paraphrases

approach

syntactic paraphrases

phrasal and single word lexical paraphrases

syntactic paraphrases


Input: An efficient bit-vector-based CKY-style parser for context-free parsing is presented. The parser computes a compact parse forest representation of the complete set of possible analyses for large treebank grammars and long input sentences. The parser uses bit-vector operations to parallelise the basic parsing operations. The parser is particularly useful when all analyses are needed rather than just the most probable one.

bit-vector-based CKY-style parser

parser

parse forest representation

large treebank grammars

parser

bit-vector operations

parser

context-free parsing

bit-vector-based CKY-style parser

context-free parsing

parser

parse forest representation

parse forest representation

large treebank grammars

bit-vector operations

parser


Input: In this paper, we propose a partially-blurred-image classification and analysis framework for automatically detecting images containing blurred regions and recognizing the blur types for those regions without needing to perform blur kernel estimation and image deblurring. We develop several blur features modeled by image color, gradient, and spectrum information, and use feature parameter training to robustly classify blurred images. Our blur detection is based on image patches, making region-wise training and classification in one image efficient. Extensive experiments show that our method works satisfactorily on challenging image data, which establishes a technical foundation for solving several computer vision problems, such as motion analysis and image restoration, using the blur information.

partially-blurred-image classification and analysis framework

feature parameter training

blur detection

region-wise training and classification

automatically detecting images

blur kernel estimation

image deblurring

computer vision problems

motion analysis

image restoration

images

blurred images

image data

blurred regions

blur features

image color

gradient

spectrum information

image patches

blur information

regions

method

partially-blurred-image classification and analysis framework

automatically detecting images

image color

blur features

gradient

blur features

spectrum information

blur features

feature parameter training

blurred images

image patches

blur detection

region-wise training and classification

blur detection

method

computer vision problems

blur information

method

blurred regions

images

blur kernel estimation

image deblurring

image color

gradient

gradient

spectrum information

motion analysis

image restoration

image data

method

motion analysis

computer vision problems

image restoration

computer vision problems


Input: It is well-known that there are polysemous words like sentence whose meaning or sense depends on the context of use. We have recently reported on two new word-sense disambiguation systems, one trained on bilingual material -LRB- the Canadian Hansards -RRB- and the other trained on monolingual material -LRB- Roget's Thesaurus and Grolier's Encyclopedia -RRB-. As this work was nearing completion, we observed a very strong discourse effect. That is, if a polysemous word such as sentence appears two or more times in a well-written discourse, it is extremely likely that they will all share the same sense. This paper describes an experiment which confirmed this hypothesis and found that the tendency to share sense in the same discourse is extremely strong -LRB- 98% -RRB-. This result can be used as an additional source of constraint for improving the performance of the word-sense disambiguation algorithm. In addition, it could also be used to help evaluate disambiguation algorithms that did not make use of the discourse constraint.

polysemous words

discourse

polysemous word

well-written discourse

discourse

constraint

discourse constraint

word-sense disambiguation systems

word-sense disambiguation algorithm

disambiguation algorithms

one

other

it

bilingual material

Canadian Hansards

monolingual material

Roget's Thesaurus

Grolier's Encyclopedia

one

word-sense disambiguation systems

other

word-sense disambiguation systems

Roget's Thesaurus

monolingual material

Grolier's Encyclopedia

monolingual material

one

other

Roget's Thesaurus

Grolier's Encyclopedia

bilingual material

one

it

disambiguation algorithms

monolingual material

other


Input: We study and compare two novel embedding methods for segmenting feature points of piece-wise planar structures from two -LRB- uncalibrated -RRB- perspective images. We show that a set of different homographies can be embedded in different ways to a higher-dimensional real or complex space, so that each homography corresponds to either a complex bilinear form or a real quadratic form. Each embedding reveals different algebraic properties and relations of homo-graphies. We give a closed-form segmentation solution for each case by utilizing these properties based on subspace-segmentation methods. These theoretical results show that one can intrinsically segment a piece-wise planar scene from 2-D images without explicitly performing any 3-D reconstruction. The resulting segmentation may make subsequent 3-D reconstruction much better-conditioned. We demonstrate the proposed methods with some convincing experimental results.

embedding methods

closed-form segmentation solution

subspace-segmentation methods

3-D reconstruction

3-D reconstruction

segmenting feature points of piece-wise planar structures

homographies

higher-dimensional real or complex space

homography

complex bilinear form

real quadratic form

homo-graphies

piece-wise planar scene

2-D images

embedding methods

segmenting feature points of piece-wise planar structures

subspace-segmentation methods

closed-form segmentation solution

higher-dimensional real or complex space

homographies

complex bilinear form

homography

real quadratic form

homography

2-D images

piece-wise planar scene

complex bilinear form

real quadratic form


Input: Background maintenance is a frequent element of video surveillance systems. We develop Wallflower, a three-component system for background maintenance: the pixel-level component performs Wiener filtering to make probabilistic predictions of the expected background; the region-level component fills in homogeneous regions of foreground objects; and the frame-level component detects sudden, global changes in the image and swaps in better approximations of the background. We compare our system with 8 other background subtraction algorithms. Wallflower is shown to outperform previous algorithms by handling a greater set of the difficult situations that can occur. Finally, we analyze the experimental results and propose normative principles for background maintenance.

Background maintenance

video surveillance systems

background maintenance

probabilistic predictions of the expected background

background maintenance

Wallflower

pixel-level component

Wiener filtering

region-level component

frame-level component

background subtraction algorithms

Wallflower

normative principles

three-component system

system

algorithms

homogeneous regions of foreground objects

Background maintenance

video surveillance systems

pixel-level component

three-component system

region-level component

three-component system

frame-level component

three-component system

three-component system

background maintenance

Wiener filtering

pixel-level component

Wiener filtering

probabilistic predictions of the expected background

region-level component

homogeneous regions of foreground objects

normative principles

background maintenance

pixel-level component

region-level component

region-level component

frame-level component

system

background subtraction algorithms

Wallflower

algorithms


Input: Is it possible to use out-of-domain acoustic training data to improve a speech recognizer's performance on a speciic, independent application? In our experiments, we use Wallstreet Journal -LRB- WSJ -RRB- data to train a recognizer, which is adapted and evaluated in the Phonebook domain. Apart from their common language -LRB- US English -RRB-, the two corpora diier in many important respects: microphone vs. telephone channel, continuous speech vs. isolated words, mismatch i n s p e a k i n g r a t e. This paper deals with two questions. First, starting from the WSJ-trained recognizer, how much adaptation data -LRB- taken from the Phonebook training corpus -RRB- is necessary to achieve a reasonable recognition performance in spite of the high degree of mismatch? Second, is it possible to improve the recognition performance of a Phonebook-trained baseline acoustic model by using additional out-of-domain training data? The paper describes the adaptation and normalization techniques used to bridge the mismatch b e-tween the two corpora.

out-of-domain acoustic training data

Wallstreet Journal -LRB- WSJ -RRB- data

Phonebook domain

adaptation data

Phonebook training corpus

out-of-domain training data

speech recognizer

recognizer

WSJ-trained recognizer

Phonebook-trained baseline acoustic model

adaptation and normalization techniques

common language -LRB- US English -RRB-

microphone vs. telephone channel

continuous speech

isolated words

mismatch

recognition

recognition

out-of-domain acoustic training data

speech recognizer

Wallstreet Journal -LRB- WSJ -RRB- data

recognizer

WSJ-trained recognizer

recognition

adaptation data

WSJ-trained recognizer

Phonebook-trained baseline acoustic model

recognition

out-of-domain training data

Phonebook-trained baseline acoustic model

Phonebook domain

recognizer

adaptation data

Phonebook training corpus


Input: This paper proposes an approach to full parsing suitable for Information Extraction from texts. Sequences of cascades of rules deterministically analyze the text, building unambiguous structures. Initially basic chunks are analyzed; then argumental relations are recognized; finally modifier attachment is performed and the global parse tree is built. The approach was proven to work for three languages and different domains. It was implemented in the IE module of FACILE, a EU project for multilingual text classification and IE.

approach

approach

It

full parsing

Information Extraction

rules

unambiguous structures

argumental relations

modifier attachment

global parse tree

IE module

FACILE, a EU project for multilingual text classification and IE

approach

full parsing

full parsing

Information Extraction

It

IE module

IE module

FACILE, a EU project for multilingual text classification and IE


Input: This paper presents a corpus study that explores the extent to which captions contribute to recognizing the intended message of an information graphic. It then presents an implemented graphic interpretation system that takes into account a variety of communicative signals, and an evaluation study showing that evidence obtained from shallow processing of the graphic's caption has a significant impact on the system's success. This work is part of a larger project whose goal is to provide sight-impaired users with effective access to information graphics.

corpus study

information graphic

sight-impaired users

information graphics

graphic interpretation system

shallow processing

communicative signals

graphic's caption

system

communicative signals

graphic interpretation system

shallow processing

system

graphic's caption

shallow processing


Input: Graphical models such as Bayesian Networks -LRB- BNs -RRB- are being increasingly applied to various computer vision problems. One bottleneck in using BN is that learning the BN model parameters often requires a large amount of reliable and representative training data, which proves to be difficult to acquire for many computer vision tasks. On the other hand, there is often available qualitative prior knowledge about the model. Such knowledge comes either from domain experts based on their experience or from various physical or geometric constraints that govern the objects we try to model. Unlike the quantitative prior, the qualitative prior is often ignored due to the difficulty of incorporating them into the model learning process. In this paper, we introduce a closed-form solution to systematically combine the limited training data with some generic qualitative knowledge for BN parameter learning. To validate our method, we compare it with the Maximum Likelihood -LRB- ML -RRB- estimation method under sparse data and with the Expectation Maximization -LRB- EM -RRB- algorithm under incomplete data respectively. To further demonstrate its applications for computer vision, we apply it to learn a BN model for facial Action Unit -LRB- AU -RRB- recognition from real image data. The experimental results show that with simple and generic qualitative constraints and using only a small amount of training data, our method can robustly and accurately estimate the BN model parameters.

Graphical models

Bayesian Networks -LRB- BNs -RRB-

BN

closed-form solution

BN parameter learning

Maximum Likelihood -LRB- ML -RRB- estimation method

Expectation Maximization -LRB- EM -RRB- algorithm

BN model

computer vision problems

computer vision tasks

model learning process

computer vision

facial Action Unit -LRB- AU -RRB- recognition

BN model parameters

qualitative prior knowledge

domain experts

physical or geometric constraints

quantitative prior

qualitative prior

qualitative knowledge

generic qualitative constraints

BN model parameters

representative training data

limited training data

sparse data

incomplete data

real image data

training data

model

knowledge

them

method

it

it

method

Graphical models

computer vision problems

representative training data

BN model parameters

representative training data

computer vision tasks

domain experts

knowledge

physical or geometric constraints

knowledge

limited training data

BN parameter learning

qualitative knowledge

BN parameter learning

BN parameter learning

closed-form solution

sparse data

it

sparse data

Maximum Likelihood -LRB- ML -RRB- estimation method

incomplete data

it

incomplete data

Expectation Maximization -LRB- EM -RRB- algorithm

it

computer vision

it

BN model

BN model

facial Action Unit -LRB- AU -RRB- recognition

real image data

facial Action Unit -LRB- AU -RRB- recognition

generic qualitative constraints

method

training data

method

method

BN model parameters

Bayesian Networks -LRB- BNs -RRB-

Graphical models

qualitative prior knowledge

model

domain experts

physical or geometric constraints

limited training data

qualitative knowledge

generic qualitative constraints

training data

quantitative prior

qualitative prior

it

Maximum Likelihood -LRB- ML -RRB- estimation method

it

Expectation Maximization -LRB- EM -RRB- algorithm

them

model learning process


Input: In this paper we introduce a modal language LT for imposing constraints on trees, and an extension LT -LRB- LF -RRB- for imposing constraints on trees decorated with feature structures. The motivation for introducing these languages is to provide tools for formalising grammatical frameworks perspicuously, and the paper illustrates this by showing how the leading ideas of GPSG can be captured in LT -LRB- LF -RRB-. In addition, the role of modal languages -LRB- and in particular, what we have called as constraint formalisms for linguistic theorising is discussed in some detail.

modal language LT

extension LT -LRB- LF -RRB-

grammatical frameworks

GPSG

LT -LRB- LF -RRB-

modal languages

constraint formalisms

constraints on trees

constraints on trees decorated with feature structures

languages

modal language LT

constraints on trees

extension LT -LRB- LF -RRB-

constraints on trees decorated with feature structures

languages

grammatical frameworks

GPSG

LT -LRB- LF -RRB-


Input: Previous research has demonstrated the utility of clustering in inducing semantic verb classes from undisambiguated corpus data. We describe a new approach which involves clustering subcategorization frame -LRB- SCF -RRB- distributions using the Information Bottleneck and nearest neighbour methods. In contrast to previous work, we particularly focus on clustering polysemic verbs. A novel evaluation scheme is proposed which accounts for the effect of polysemy on the clusters, offering us a good insight into the potential and limitations of semantically classifying undisambiguated SCF data.

clustering

Information Bottleneck and nearest neighbour methods

inducing semantic verb classes

clustering subcategorization frame -LRB- SCF -RRB- distributions

clustering polysemic verbs

semantically classifying undisambiguated SCF data

undisambiguated corpus data

approach

evaluation scheme

polysemic verbs

polysemy

clusters

clustering

inducing semantic verb classes

undisambiguated corpus data

clustering

Information Bottleneck and nearest neighbour methods

clustering subcategorization frame -LRB- SCF -RRB- distributions

evaluation scheme

polysemy

clustering subcategorization frame -LRB- SCF -RRB- distributions

approach

evaluation scheme

semantically classifying undisambiguated SCF data

polysemy

clusters


Input: Due to the capacity of pan-tilt-zoom -LRB- PTZ -RRB- cameras to simultaneously cover a panoramic area and maintain high resolution imagery, researches in automated surveillance systems with multiple PTZ cameras have become increasingly important. Most existing algorithms require the prior knowledge of intrinsic parameters of the PTZ camera to infer the relative positioning and orientation among multiple PTZ cameras. To overcome this limitation, we propose a novel mapping algorithm that derives the relative positioning and orientation between two PTZ cameras based on a unified polynomial model. This reduces the dependence on the knowledge of intrinsic parameters of PTZ camera and relative positions. Experimental results demonstrate that our proposed algorithm presents substantially reduced computational complexity and improved flexibility at the cost of slightly decreased pixel accuracy, as compared with the work of Chen and Wang. This slightly decreased pixel accuracy can be compensated by consistent labeling approaches without added cost for the application of automated surveillance systems along with changing configurations and a larger number of PTZ cameras.

pan-tilt-zoom -LRB- PTZ -RRB- cameras

panoramic area

high resolution imagery

PTZ cameras

prior knowledge of intrinsic parameters of the PTZ camera

relative positioning

orientation

PTZ cameras

relative positioning

orientation

PTZ cameras

PTZ camera

relative positions

PTZ cameras

automated surveillance systems

automated surveillance systems

algorithms

algorithm

mapping algorithm

unified polynomial model

consistent labeling approaches

computational complexity

flexibility

pixel accuracy

pixel accuracy

pan-tilt-zoom -LRB- PTZ -RRB- cameras

panoramic area

pan-tilt-zoom -LRB- PTZ -RRB- cameras

high resolution imagery

algorithms

relative positioning

algorithms

orientation

prior knowledge of intrinsic parameters of the PTZ camera

algorithms

mapping algorithm

relative positioning

mapping algorithm

orientation

unified polynomial model

mapping algorithm

consistent labeling approaches

pixel accuracy

PTZ cameras

automated surveillance systems

relative positioning

PTZ cameras

orientation

PTZ cameras

relative positioning

PTZ cameras

orientation

PTZ cameras

relative positioning

orientation

relative positioning

orientation

computational complexity

algorithm

flexibility

algorithm

pixel accuracy

algorithm


Input: This paper presents a new two-pass algorithm for Extra Large -LRB- more than 1M words -RRB- Vocabulary COntinuous Speech recognition based on the Information Retrieval -LRB- ELVIRCOS -RRB-. The principle of this approach is to decompose a recognition process into two passes where the first pass builds the words subset for the second pass recognition by using information retrieval procedure. Word graph composition for continuous speech is presented. With this approach a high performances for large vocabulary speech recognition can be obtained.

two-pass algorithm

approach

passes

first pass

second pass recognition

approach

Extra Large -LRB- more than 1M words -RRB- Vocabulary COntinuous Speech recognition

Information Retrieval -LRB- ELVIRCOS -RRB-

large vocabulary speech recognition

recognition process

information retrieval procedure

Word graph composition

words subset

continuous speech

two-pass algorithm

Extra Large -LRB- more than 1M words -RRB- Vocabulary COntinuous Speech recognition

Information Retrieval -LRB- ELVIRCOS -RRB-

two-pass algorithm

information retrieval procedure

second pass recognition

Word graph composition

continuous speech

approach

large vocabulary speech recognition

first pass

passes

second pass recognition

passes


Input: This paper describes our work on classification of outdoor scenes. First, images are partitioned into regions using one-class classification and patch-based clustering algorithms where one-class classifiers model the regions with relatively uniform color and texture properties, and clustering of patches aims to detect structures in the remaining regions. Next, the resulting regions are clustered to obtain a codebook of region types, and two models are constructed for scene representation: a'' bag of individual regions'' representation where each region is regarded separately, and a'' bag of region pairs'' representation where regions with particular spatial relationships are considered together. Given these representations, scene classification is done using Bayesian classifiers. We also propose a novel region selection algorithm that identifies region types that are frequently found in a particular class of scenes but rarely exist in other classes, and also consistently occur together in the same class of scenes. Experiments on the LabelMe data set showed that the proposed models significantly out-perform a baseline global feature-based approach.

classification of outdoor scenes

scene classification

one-class classification

patch-based clustering algorithms

one-class classifiers

clustering of patches

scene representation

Bayesian classifiers

region selection algorithm

baseline global feature-based approach

uniform color and texture properties

codebook of region types

spatial relationships

models

models

LabelMe data set

patch-based clustering algorithms

one-class classification

uniform color and texture properties

one-class classifiers

models

scene representation

Bayesian classifiers

scene classification

LabelMe data set

models

LabelMe data set

baseline global feature-based approach

models

baseline global feature-based approach


Input: In this paper, we introduce a generative probabilistic optical character recognition -LRB- OCR -RRB- model that describes an end-to-end process in the noisy channel framework, progressing from generation of true text through its transformation into the noisy output of an OCR system. The model is designed for use in error correction, with a focus on post-processing the output of black-box OCR systems in order to make it more useful for NLP tasks. We present an implementation of the model based on finite-state models, demonstrate the model's ability to significantly reduce character and word error rate, and provide evaluation results involving automatic extraction of translation lexicons from printed text.

generative probabilistic optical character recognition -LRB- OCR -RRB- model

noisy channel framework

finite-state models

OCR system

output of black-box OCR systems

OCR systems

model

it

model

model

error correction

post-processing

NLP tasks

automatic extraction of translation lexicons

character and word error rate

printed text

model

error correction

model

post-processing

it

NLP tasks

finite-state models

model

printed text

automatic extraction of translation lexicons

post-processing

error correction

character and word error rate

model

automatic extraction of translation lexicons

model


Input: We present a framework for word alignment based on log-linear models. All knowledge sources are treated as feature functions, which depend on the source langauge sentence, the target language sentence and possible additional variables. Log-linear models allow statistical alignment models to be easily extended by incorporating syntactic information. In this paper, we use IBM Model 3 alignment probabilities, POS correspondence, and bilingual dictionary coverage as features. Our experiments show that log-linear models significantly outperform IBM translation models.

framework

word alignment

log-linear models

Log-linear models

statistical alignment models

log-linear models

IBM translation models

knowledge sources

feature functions

syntactic information

IBM Model 3 alignment probabilities

POS correspondence

bilingual dictionary coverage

features

framework

word alignment

log-linear models

framework

knowledge sources

feature functions

Log-linear models

statistical alignment models

syntactic information

Log-linear models

IBM Model 3 alignment probabilities

features

POS correspondence

features

bilingual dictionary coverage

features

IBM Model 3 alignment probabilities

POS correspondence

POS correspondence

bilingual dictionary coverage

log-linear models

IBM translation models


Input: Hough voting in a geometric transformation space allows us to realize spatial verification, but remains sensitive to feature detection errors because of the inflexible quan-tization of single feature correspondences. To handle this problem, we propose a new method, called adaptive dither voting, for robust spatial verification. For each correspondence, instead of hard-mapping it to a single transformation, the method augments its description by using multiple dithered transformations that are deterministically generated by the other correspondences. The method reduces the probability of losing correspondences during transformation quantization, and provides high robustness as regards mismatches by imposing three geometric constraints on the dithering process. We also propose exploiting the non-uniformity of a Hough histogram as the spatial similarity to handle multiple matching surfaces. Extensive experiments conducted on four datasets show the superiority of our method. The method outperforms its state-of-the-art counterparts in both accuracy and scalability, especially when it comes to the retrieval of small, rotated objects.

Hough voting

adaptive dither voting

Hough histogram

geometric transformation space

feature detection errors

inflexible quan-tization of single feature correspondences

multiple dithered transformations

transformation quantization

regards mismatches

geometric constraints

dithering process

non-uniformity

spatial similarity

multiple matching surfaces

spatial verification

robust spatial verification

retrieval of small, rotated objects

method

method

method

method

method

counterparts

accuracy

scalability

Hough voting

spatial verification

method

robust spatial verification

multiple dithered transformations

method

non-uniformity

multiple matching surfaces

method

retrieval of small, rotated objects

geometric transformation space

Hough voting

non-uniformity

Hough histogram

counterparts

method

accuracy

method

accuracy

counterparts

scalability

method

scalability

counterparts


Input: We propose a novel technique called bispectral photo-metric stereo that makes effective use of fluorescence for shape reconstruction. Fluorescence is a common phenomenon occurring in many objects from natural gems and corals, to fluorescent dyes used in clothing. One of the important characteristics of fluorescence is its wavelength-shifting behavior: fluorescent materials absorb light at a certain wavelength and then reemit it at longer wavelengths. Due to the complexity of its emission process, fluo-rescence tends to be excluded from most algorithms in computer vision and image processing. In this paper, we show that there is a strong similarity between fluorescence and ideal diffuse reflection and that fluorescence can provide distinct clues on how to estimate an object's shape. Moreover, fluorescence's wavelength-shifting property enables us to estimate the shape of an object by applying photomet-ric stereo to emission-only images without suffering from specular reflection. This is the significant advantage of the fluorescence-based method over previous methods based on reflection.

technique

algorithms

methods

bispectral photo-metric stereo

emission process

photomet-ric stereo

fluorescence-based method

fluorescence

Fluorescence

natural gems

fluorescent dyes

fluorescence

fluorescent materials

fluo-rescence

similarity

fluorescence

diffuse reflection

fluorescence

fluorescence's wavelength-shifting property

shape

specular reflection

reflection

shape reconstruction

computer vision

image processing

complexity

emission-only images

bispectral photo-metric stereo

shape reconstruction

fluorescence

bispectral photo-metric stereo

algorithms

computer vision

algorithms

image processing

fluorescence's wavelength-shifting property

shape

photomet-ric stereo

shape

emission-only images

photomet-ric stereo

complexity

emission process

computer vision

image processing

methods

fluorescence-based method


Input: In this paper, we present an approach for learning a visual representation from the raw spatiotemporal signals in videos. Our representation is learned without supervision from semantic labels. We formulate our method as an unsupervised sequential verification task, i.e., we determine whether a sequence of frames from a video is in the correct temporal order. With this simple task and no semantic labels, we learn a powerful visual representation using a Convolutional Neural Network -LRB- CNN -RRB-. The representation contains complementary information to that learned from supervised image datasets like ImageNet. Qualitative results show that our method captures information that is temporally varying, such as human pose. When used as pre-training for action recognition, our method gives significant gains over learning without external data on benchmark datasets like UCF101 and HMDB51. To demonstrate its sensitivity to human pose, we show results for pose estimation on the FLIC and MPII datasets that are competitive, or better than approaches using significantly more supervision. Our method can be combined with supervised representations to provide an additional boost in accuracy.

approach

representation

method

task

representation

method

benchmark datasets

approaches

visual representation

visual representation

Convolutional Neural Network -LRB- CNN -RRB-

pre-training

our method

learning without external data

supervised representations

raw spatiotemporal signals in videos

supervised image datasets

ImageNet

FLIC and MPII datasets

supervision from semantic labels

temporal order

semantic labels

complementary information

human pose

human pose

supervision

Our method

unsupervised sequential verification task

action recognition

pose estimation

accuracy

approach

visual representation

raw spatiotemporal signals in videos

visual representation

unsupervised sequential verification task

method

task

visual representation

Convolutional Neural Network -LRB- CNN -RRB-

visual representation

supervised image datasets

complementary information

method

human pose

pre-training

action recognition

our method

pre-training

supervision

approaches

complementary information

representation

ImageNet

supervised image datasets

benchmark datasets

benchmark datasets

our method

learning without external data

benchmark datasets

our method

benchmark datasets

learning without external data

FLIC and MPII datasets

pose estimation

accuracy

Our method

supervised representations

Our method


Input: ``To explain complex phenomena, an explanation system must be able to select information from a formal representation of domain knowledge, organize the selected information into multisentential discourse plans, and realize the discourse plans in text. Although recent years have witnessed significant progress in the development of sophisticated computational mechanisms for explanation, empirical results have been limited. This paper reports on a seven-year effort to empirically study explanation generation from semantically rich, large-scale knowledge bases. In particular, it describes a robust explanation system that constructs multisentential and multi-paragraph explanations from the a large-scale knowledge base in the domain of botanical anatomy, physiology, and development. We introduce the evaluation methodology and describe how performance was assessed with this methodology in the most extensive empirical evaluation conducted on an explanation system. In this evaluation, scored within'' ``half a grade'''' of domain experts, and its performance exceeded that of one of the domain experts.''

explanation system

robust explanation system

explanation system

multisentential discourse plans

discourse plans

multisentential and multi-paragraph explanations

explanation generation

semantically rich, large-scale knowledge bases

large-scale knowledge base

botanical anatomy

physiology

development

evaluation methodology

methodology

evaluation

explanation system

discourse plans

semantically rich, large-scale knowledge bases

explanation generation

robust explanation system

multisentential and multi-paragraph explanations

large-scale knowledge base

robust explanation system

botanical anatomy

large-scale knowledge base

physiology

large-scale knowledge base

development

large-scale knowledge base

botanical anatomy

physiology

physiology

development

methodology

explanation system


Input: We present an operable definition of focus which is argued to be of a cognito-pragmatic nature and explore how it is determined in discourse in a formalized manner. For this purpose, a file card model of discourse model and knowledge store is introduced enabling the decomposition and formal representation of its determination process as a programmable algorithm -LRB- FDA -RRB-. Interdisciplinary evidence from social and cognitive psychology is cited and the prospect of the integration of focus via FDA as a discourse-level construct into speech synthesis systems, in particular, concept-to-speech systems, is also briefly discussed.

operable definition

it

cognito-pragmatic nature

discourse

knowledge store

determination process

social and cognitive psychology

FDA

discourse-level construct

a file card model of discourse model and knowledge store

discourse model

programmable algorithm

FDA

speech synthesis systems

concept-to-speech systems

cognito-pragmatic nature

operable definition

FDA

programmable algorithm

concept-to-speech systems

speech synthesis systems

FDA

discourse-level construct

discourse-level construct

speech synthesis systems


Input: Conditional Random Field models have proved effective for several low-level computer vision problems. Inference in these models involves solving a combinatorial optimization problem, with methods such as graph cuts, belief propagation. Although several methods have been proposed to learn the model parameters from training data, they suffer from various drawbacks. Learning these parameters involves computing the partition function, which is intractable. To overcome this, state-of-the-art structured learning methods frame the problem as one of large margin estimation. Iterative solutions have been proposed to solve the resulting convex optimization problem. Each iteration involves solving an inference problem over all the labels, which limits the efficiency of these structured methods. In this paper we present an efficient large margin piece-wise learning method which is widely applicable. We show how the resulting optimization problem can be reduced to an equivalent convex problem with a small number of constraints, and solve it using an efficient scheme. Our method is both memory and computationally efficient. We show results on publicly available standard datasets.

Conditional Random Field models

graph cuts

belief propagation

structured learning methods

Iterative solutions

large margin piece-wise learning method

low-level computer vision problems

Inference

combinatorial optimization problem

large margin estimation

convex optimization problem

inference problem

optimization problem

convex problem

models

methods

parameters

problem

structured methods

method

model parameters

partition function

Inference

models

methods

combinatorial optimization problem

graph cuts

methods

belief propagation

methods

structured learning methods

problem

large margin estimation

problem

Iterative solutions

convex optimization problem

convex problem

optimization problem

combinatorial optimization problem

Inference

graph cuts

belief propagation


Input: Interpreting metaphors is an integral and inescapable process in human understanding of natural language. This paper discusses a method of analyzing metaphors based on the existence of a small number of generalized metaphor mappings. Each generalized metaphor contains a recognition network, a basic mapping, additional transfer mappings, and an implicit intention component. It is argued that the method reduces metaphor interpretation from a reconstruction to a recognition task. Implications towards automating certain aspects of language learning are also discussed.

Interpreting metaphors

human understanding of natural language

analyzing metaphors

metaphor interpretation

reconstruction

recognition task

language learning

method

method

generalized metaphor mappings

recognition network

basic mapping

transfer mappings

implicit intention component

generalized metaphor

Interpreting metaphors

human understanding of natural language

method

analyzing metaphors

generalized metaphor mappings

analyzing metaphors

method

metaphor interpretation

recognition task

metaphor interpretation

recognition network

generalized metaphor

basic mapping

generalized metaphor

transfer mappings

generalized metaphor

implicit intention component

generalized metaphor

recognition network

basic mapping

transfer mappings

basic mapping

transfer mappings

implicit intention component


Input: This study presents a method to automatically acquire paraphrases using bilingual corpora, which utilizes the bilingual dependency relations obtained by projecting a monolingual dependency parse onto the other language sentence based on statistical alignment techniques. Since the paraphrasing method is capable of clearly disambiguating the sense of an original phrase using the bilingual context of dependency relation, it would be possible to obtain interchangeable paraphrases under a given context. Also, we provide an advanced method to acquire generalized translation knowledge using the extracted paraphrases. We applied the method to acquire the generalized translation knowledge for Korean-English translation. Through experiments with parallel corpora of a Korean and English language pairs, we show that our paraphrasing method effectively extracts paraphrases with high precision, 94.3% and 84.6% respectively for Korean and English, and the translation knowledge extracted from the bilingual corpora could be generalized successfully using the paraphrases with the 12.5% compression ratio.

method to automatically acquire paraphrases

statistical alignment techniques

paraphrasing method

paraphrasing method

bilingual corpora

parallel corpora of a Korean and English language pairs

Korean

English

bilingual corpora

bilingual dependency relations

monolingual dependency parse

bilingual context of dependency relation

paraphrases

generalized translation knowledge

paraphrases

generalized translation knowledge

paraphrases

translation knowledge

paraphrases

method

method

Korean-English translation

precision

compression ratio

bilingual corpora

method to automatically acquire paraphrases

bilingual dependency relations

method to automatically acquire paraphrases

monolingual dependency parse

bilingual dependency relations

statistical alignment techniques

bilingual dependency relations

bilingual context of dependency relation

paraphrasing method

method

generalized translation knowledge

paraphrases

method

method

generalized translation knowledge

generalized translation knowledge

Korean-English translation

paraphrasing method

paraphrases

bilingual corpora

translation knowledge

paraphrases

translation knowledge

precision

paraphrasing method

compression ratio

translation knowledge

Korean

English


Input: We provide a logical definition of Minimalist grammars, that are Stabler's formalization of Chomsky's minimalist program. Our logical definition leads to a neat relation to categorial grammar, -LRB- yielding a treatment of Montague semantics -RRB-, a parsing-as-deduction in a resource sensitive logic, and a learning algorithm from structured data -LRB- based on a typing-algorithm and type-unification -RRB-. Here we emphasize the connection to Montague semantics which can be viewed as a formal computation of the logical form.

logical definition of Minimalist grammars

Stabler's formalization of Chomsky's minimalist program

logical definition

categorial grammar

parsing-as-deduction

learning algorithm

typing-algorithm

type-unification

Montague semantics

resource sensitive logic

Montague semantics

formal computation of the logical form

logical form

structured data

Stabler's formalization of Chomsky's minimalist program

logical definition of Minimalist grammars

logical definition

Montague semantics

parsing-as-deduction

resource sensitive logic

structured data

learning algorithm

typing-algorithm

learning algorithm

type-unification

learning algorithm

typing-algorithm

type-unification


Input: There are several approaches that model information extraction as a token classification task, using various tagging strategies to combine multiple tokens. We describe the tagging strategies that can be found in the literature and evaluate their relative performances. We also introduce a new strategy, called Begin/After tagging or BIA, and show that it is competitive to the best other strategies.

approaches

strategy

it

strategies

information extraction

token classification task

tagging strategies

tagging strategies

Begin/After tagging

BIA

approaches

information extraction

tagging strategies

token classification task

information extraction

token classification task

it

strategies


Input: The PRC Adaptive Knowledge-based Text Understanding System -LRB- PAKTUS -RRB- has been under development as an Independent Research and Development project at PRC since 1984. The objective is a generic system of tools, including a core English lexicon, grammar, and concept representations, for building natural language processing -LRB- NLP -RRB- systems for text understanding. Systems built with PAKTUS are intended to generate input to knowledge based systems ordata base systems. Input to the NLP system is typically derived from an existing electronic message stream, such as a news wire. PAKTUS supports the adaptation of the generic core to a variety of domains: JINTACCS messages, RAINFORM messages, news reports about a specific type of event, such as financial transfers or terrorist acts, etc., by acquiring sublanguage and domain-specific grammar, words, conceptual mappings, and discourse patterns. The long-term goal is a system that can support the processing of relatively long discourses in domains that are fairly broad with a high rate of success.

PRC Adaptive Knowledge-based Text Understanding System -LRB- PAKTUS -RRB-

grammar

concept representations

natural language processing -LRB- NLP -RRB- systems

PAKTUS

NLP system

PAKTUS

sublanguage and domain-specific grammar

system

words

core English lexicon

news reports

event

conceptual mappings

discourse patterns

text understanding

knowledge based systems

electronic message stream

news wire

JINTACCS messages

RAINFORM messages

financial transfers

terrorist acts

system

natural language processing -LRB- NLP -RRB- systems

natural language processing -LRB- NLP -RRB- systems

text understanding

PAKTUS

knowledge based systems

electronic message stream

NLP system

PAKTUS

JINTACCS messages

PAKTUS

RAINFORM messages

PAKTUS

news reports

sublanguage and domain-specific grammar

PAKTUS

words

PAKTUS

conceptual mappings

PAKTUS

discourse patterns

PAKTUS

core English lexicon

system

grammar

system

concept representations

system

news wire

electronic message stream

financial transfers

event

terrorist acts

event

JINTACCS messages

RAINFORM messages

RAINFORM messages

news reports

financial transfers

terrorist acts

sublanguage and domain-specific grammar

words

words

conceptual mappings

conceptual mappings

discourse patterns

event

news reports


Input: A major axis of research at LIMSI is directed at multilingual, speaker-independent, large vocabulary speech dictation. In this paper the LIMSI recognizer which was evaluated in the ARPA NOV93 CSR test is described, and experimental results on the WSJ and BREF corpora under closely matched conditions are reported. For both corpora word recognition experiments were carried out with vocabularies containing up to 20k words. The recognizer makes use of continuous density HMM with Gaussian mixture for acoustic modeling and n-gram statistics estimated on the newspaper texts for language modeling. The recognizer uses a time-synchronous graph-search strategy which is shown to still be viable with a 20k-word vocabulary when used with bigram back-off language models. A second forward pass, which makes use of a word graph generated with the bigram, incorporates a trigram language model. Acoustic modeling uses cepstrum-based features, context-dependent phone models -LRB- intra and interword -RRB-, phone duration models, and sex-dependent models.

multilingual, speaker-independent, large vocabulary speech dictation

word recognition

acoustic modeling

language modeling

Acoustic modeling

LIMSI recognizer

continuous density HMM

Gaussian mixture

n-gram statistics

time-synchronous graph-search strategy

bigram back-off language models

bigram

trigram language model

context-dependent phone models -LRB- intra and interword -RRB-

phone duration models

sex-dependent models

ARPA NOV93 CSR test

WSJ and BREF corpora

newspaper texts

corpora

recognizer

recognizer

word graph

cepstrum-based features

ARPA NOV93 CSR test

LIMSI recognizer

WSJ and BREF corpora

LIMSI recognizer

corpora

word recognition

newspaper texts

n-gram statistics

continuous density HMM

recognizer

continuous density HMM

acoustic modeling

Gaussian mixture

acoustic modeling

n-gram statistics

recognizer

n-gram statistics

language modeling

time-synchronous graph-search strategy

recognizer

bigram back-off language models

recognizer

bigram

word graph

cepstrum-based features

Acoustic modeling

context-dependent phone models -LRB- intra and interword -RRB-

Acoustic modeling

phone duration models

Acoustic modeling

sex-dependent models

Acoustic modeling

continuous density HMM

Gaussian mixture

continuous density HMM

n-gram statistics

bigram back-off language models

time-synchronous graph-search strategy

trigram language model

word graph

cepstrum-based features

context-dependent phone models -LRB- intra and interword -RRB-

context-dependent phone models -LRB- intra and interword -RRB-

phone duration models

phone duration models

sex-dependent models


Input: The co-occurrence pattern, a combination of binary or local features, is more discriminative than individual features and has shown its advantages in object, scene, and action recognition. We discuss two types of co-occurrence patterns that are complementary to each other, the conjunction -LRB- AND -RRB- and disjunction -LRB- OR -RRB- of binary features. The necessary condition of identifying discriminative co-occurrence patterns is firstly provided. Then we propose a novel data mining method to efficiently discover the optimal co-occurrence pattern with minimum empirical error, despite the noisy training dataset. This mining procedure of AND and OR patterns is readily integrated to boosting, which improves the generalization ability over the conventional boosting decision trees and boosting decision stumps. Our versatile experiments on object, scene, and action cat-egorization validate the advantages of the discovered dis-criminative co-occurrence patterns.

co-occurrence pattern

binary or local features

co-occurrence patterns

conjunction -LRB- AND -RRB- and disjunction -LRB- OR -RRB- of binary features

discriminative co-occurrence patterns

optimal co-occurrence pattern

minimum empirical error

AND and OR patterns

dis-criminative co-occurrence patterns

object, scene, and action recognition

identifying discriminative co-occurrence patterns

object, scene, and action cat-egorization

data mining method

boosting

boosting decision trees

boosting decision stumps

noisy training dataset

mining procedure

generalization ability

co-occurrence pattern

object, scene, and action recognition

data mining method

optimal co-occurrence pattern

noisy training dataset

data mining method

mining procedure

AND and OR patterns

binary or local features

co-occurrence pattern

AND and OR patterns

boosting

minimum empirical error

optimal co-occurrence pattern

boosting

boosting decision trees

boosting

boosting decision stumps

generalization ability

boosting

generalization ability

boosting decision trees

generalization ability

boosting decision stumps

object, scene, and action cat-egorization

dis-criminative co-occurrence patterns

boosting decision trees

boosting decision stumps


Input: Empirical experience and observations have shown us when powerful and highly tunable classifiers such as maximum entropy classifiers, boosting and SVMs are applied to language processing tasks, it is possible to achieve high accuracies, but eventually their performances all tend to plateau out at around the same point. To further improve performance, various error correction mechanisms have been developed, but in practice, most of them can not be relied on to predictably improve performance on unseen data; indeed, depending upon the test set, they are as likely to degrade accuracy as to improve it. This problem is especially severe if the base classifier has already been finely tuned. In recent work, we introduced N-fold Templated Piped Correction, or NTPC -LRB- ``nitpick'' -RRB-, an intriguing error corrector that is designed to work in these extreme operating conditions. Despite its simplicity, it consistently and robustly improves the accuracy of existing highly accurate base models. This paper investigates some of the more surprising claims made by NTPC, and presents experiments supporting an Occam's Razor argument that more complex models are damaging or unnecessary in practice.

classifiers

maximum entropy classifiers

boosting

SVMs

error correction mechanisms

N-fold Templated Piped Correction, or NTPC -LRB- ``nitpick'' -RRB-

error corrector

NTPC

language processing tasks

base classifier

it

base models

Occam's Razor argument

classifiers

language processing tasks

maximum entropy classifiers

classifiers

boosting

classifiers

SVMs

classifiers

N-fold Templated Piped Correction, or NTPC -LRB- ``nitpick'' -RRB-

error corrector

maximum entropy classifiers

boosting

boosting

SVMs

it

base models


Input: A flexible parser can deal with input that deviates from its grammar, in addition to input that conforms to it. Ideally, such a parser will correct the deviant input: sometimes, it will be unable to correct it at all; at other times, correction will be possible, but only to within a range of ambiguous possibilities. This paper is concerned with such ambiguous situations, and with making it as easy as possible for the ambiguity to be resolved through consultation with the user of the parser-we presume interactive use. We show the importance of asking the user for clarification in as focused a way as possible. Focused interaction of this kind is facilitated by a construction-specific approach to flexible parsing, with specialized parsing techniques for each type of construction, and specialized ambiguity representations for each type of ambiguity that a particular construction can give rise to. A construction-specific approach also aids in task-specific language development by allowing a language definition that is natural in terms of the task domain to be interpreted directly without compilation into a uniform grammar formalism, thus greatly speeding the testing of changes to the language definition.

flexible parser

parser

parser

construction-specific approach

specialized parsing techniques

ambiguity representations

construction-specific approach

uniform grammar formalism

it

ambiguity

ambiguity

flexible parsing

construction

construction

task-specific language development

construction-specific approach

flexible parsing

specialized parsing techniques

construction

ambiguity representations

ambiguity

construction-specific approach

task-specific language development

construction-specific approach

specialized parsing techniques

specialized parsing techniques

ambiguity representations


Input: A proposal to deal with French tenses in the framework of Discourse Representation Theory is presented, as it has been implemented for a fragment at the IMS. It is based on the theory of tenses of H. Kamp and Ch. Rohrer. Instead of using operators to express the meaning of the tenses the Reichenbachian point of view is adopted and refined such that the impact of the tenses with respect to the meaning of the text is understood as contribution to the integration of the events of a sentence in the event structure of the preceeding text. Thereby a system of relevant times provided by the preceeding text and by the temporal adverbials of the sentence being processed is used. This system consists of one or more reference times and temporal perspective times, the speech time and the location time. The special interest of our proposal is to establish a plausible choice of anchors for the new event out of the system of relevant times and to update this system of temporal coordinates correctly. The problem of choice is largely neglected in the literature. In opposition to the approach of Kamp and Rohrer the exact meaning of the tenses is fixed by the resolution component and not in the process of syntactic analysis.

proposal

it

It

operators

system

French tenses

theory of tenses

meaning of the tenses

event structure

preceeding text

temporal adverbials

reference times

temporal perspective times

speech time

location time

meaning of the tenses

Discourse Representation Theory

system of relevant times

system of relevant times

system of temporal coordinates

resolution component

syntactic analysis

IMS

Discourse Representation Theory

French tenses

it

IMS

theory of tenses

It

operators

meaning of the tenses

preceeding text

system of relevant times

temporal adverbials

system of relevant times

resolution component

meaning of the tenses

preceeding text

temporal adverbials

reference times

temporal perspective times

temporal perspective times

speech time

speech time

location time

reference times

system

temporal perspective times

system

speech time

system

location time

system

resolution component

syntactic analysis


Input: The work presented in this paper is the first step in a project which aims to cluster and summarise electronic discussions in the context of help-desk applications. The eventual objective of this project is to use these summaries to assist help-desk users and operators. In this paper, we identify features of electronic discussions that influence the clustering process, and offer a filtering mechanism that removes undesirable influences. We tested the clustering and filtering processes on electronic newsgroup discussions, and evaluated their performance by means of two experiments: coarse-level clustering simple information retrieval.

electronic discussions

help-desk applications

electronic discussions

coarse-level clustering

information retrieval

features

clustering process

filtering mechanism

clustering and filtering processes

electronic newsgroup discussions

experiments

electronic discussions

help-desk applications

features

electronic discussions

electronic newsgroup discussions

clustering and filtering processes

experiments

clustering and filtering processes

experiments

clustering and filtering processes

coarse-level clustering

experiments

information retrieval

experiments


Input: The paper presents a method for word sense disambiguation based on parallel corpora. The method exploits recent advances in word alignment and word clustering based on automatic extraction of translation equivalents and being supported by available aligned wordnets for the languages in the corpus. The wordnets are aligned to the Princeton Wordnet, according to the principles established by EuroWordNet. The evaluation of the WSD system, implementing the method described herein showed very encouraging results. The same system used in a validation mode, can be used to check and spot alignment errors in multilingually aligned wordnets as BalkaNet and EuroWordNet.

method

method

method

system

word sense disambiguation

word alignment

word clustering

automatic extraction of translation equivalents

parallel corpora

aligned wordnets

wordnets

Princeton Wordnet

EuroWordNet

multilingually aligned wordnets

BalkaNet

EuroWordNet

WSD system

alignment errors in multilingually aligned wordnets

method

word sense disambiguation

parallel corpora

method

method

word alignment

method

word clustering

automatic extraction of translation equivalents

method

aligned wordnets

method

system

alignment errors in multilingually aligned wordnets

word alignment

word clustering

BalkaNet

EuroWordNet

BalkaNet

multilingually aligned wordnets

EuroWordNet

multilingually aligned wordnets


Input: This paper investigates critical configurations for projective reconstruction from multiple images taken by a camera moving in a straight line. Projective reconstruction refers to a determination of the 3D geometrical configuration of a set of 3D points and cameras, given only correspondences between points in the images. A configuration of points and cameras is critical if it can not be determined uniquely -LRB- up to a projective transform -RRB- from the image coordinates of the points. It is shown that a configuration consisting of any number of cameras lying on a straight line, and any number of points lying on a twisted cubic constitutes a critical configuration. An alternative configuration consisting of a set of points and cameras all lying on a rational quartic curve exists.

projective reconstruction

Projective reconstruction

images

3D geometrical configuration

3D points and cameras

image coordinates

rational quartic curve

images

projective reconstruction

3D geometrical configuration

3D points and cameras


Input: Porting a Natural Language Processing -LRB- NLP -RRB- system to a new domain remains one of the bottlenecks in syntactic parsing, because of the amount of effort required to fix gaps in the lexicon, and to attune the existing grammar to the idiosyncracies of the new sublanguage. This paper shows how the process of fitting a lexicalized grammar to a domain can be automated to a great extent by using a hybrid system that combines traditional knowledge-based techniques with a corpus-based approach.

Natural Language Processing -LRB- NLP -RRB- system

syntactic parsing

new domain

grammar

lexicalized grammar

hybrid system

knowledge-based techniques

corpus-based approach

idiosyncracies of the new sublanguage

Natural Language Processing -LRB- NLP -RRB- system

new domain

grammar

idiosyncracies of the new sublanguage

knowledge-based techniques

hybrid system

corpus-based approach

hybrid system

knowledge-based techniques

corpus-based approach


Input: Chart parsing is directional in the sense that it works from the starting point -LRB- usually the beginning of the sentence -RRB- extending its activity usually in a rightward manner. We shall introduce the concept of a chart that works outward from islands and makes sense of as much of the sentence as it is actually possible, and after that will lead to predictions of missing fragments. So, for any place where the easily identifiable fragments occur in the sentence, the process will extend to both the left and the right of the islands, until possibly completely missing fragments are reached. At that point, by virtue of the fact that both a left and a right context were found, heuristics can be introduced that predict the nature of the missing fragments.


Input: Unification is often the appropriate method for expressing relations between representations in the form of feature structures; however, there are circumstances in which a different approach is desirable. A declarative formalism is presented which permits direct mappings of one feature structure into another, and illustrative examples are given of its application to areas of current interest.

Unification

declarative formalism

method

approach

relations between representations

feature structures

direct mappings of one feature structure into another

method

relations between representations

feature structures

relations between representations

approach

method

direct mappings of one feature structure into another

declarative formalism


Input: To support engaging human users in robust, mixed-initiative speech dialogue interactions which reach beyond current capabilities in dialogue systems, the DARPA Communicator program -LSB- 1 -RSB- is funding the development of a distributed message-passing infrastructure for dialogue systems which all Communicator participants are using. In this presentation, we describe the features of and requirements for a genuinely useful software infrastructure for this purpose.

mixed-initiative speech dialogue interactions

dialogue systems

distributed message-passing infrastructure

dialogue systems

distributed message-passing infrastructure

dialogue systems


Input: We propose a novel limited-memory stochastic block BFGS update for incorporating enriched curvature information in stochastic approximation methods. In our method, the estimate of the inverse Hessian matrix that is maintained by it, is updated at each iteration using a sketch of the Hessian, i.e., a randomly generated compressed form of the Hessian. We propose several sketching strategies, present a new quasi-Newton method that uses stochastic block BFGS updates combined with the variance reduction approach SVRG to compute batch stochastic gradients, and prove linear convergence of the resulting method. Numerical tests on large-scale logistic regression problems reveal that our method is more robust and substantially outperforms current state-of-the-art methods.

limited-memory stochastic block BFGS update

sketching strategies

quasi-Newton method

stochastic block BFGS updates

variance reduction approach SVRG

incorporating enriched curvature information in stochastic approximation methods

large-scale logistic regression problems

method

it

method

method

state-of-the-art methods

inverse Hessian matrix

Hessian

randomly generated compressed form of the Hessian

batch stochastic gradients

linear convergence

limited-memory stochastic block BFGS update

incorporating enriched curvature information in stochastic approximation methods

it

inverse Hessian matrix

Hessian

it

quasi-Newton method

batch stochastic gradients

stochastic block BFGS updates

quasi-Newton method

variance reduction approach SVRG

quasi-Newton method

randomly generated compressed form of the Hessian

Hessian

stochastic block BFGS updates

variance reduction approach SVRG

linear convergence

method

large-scale logistic regression problems

method

large-scale logistic regression problems

state-of-the-art methods

method

state-of-the-art methods