Input: This paper presents an algorithm for computing optical flow, shape, motion, lighting, and albedo from an image sequence of a rigidly-moving Lambertian object under distant illumination. The problem is formulated in a manner that subsumes structure from motion, multi-view stereo, and photo-metric stereo as special cases. The algorithm utilizes both spatial and temporal intensity variation as cues: the former constrains flow and the latter constrains surface orientation; combining both cues enables dense reconstruction of both textured and texture-less surfaces. The algorithm works by iteratively estimating affine camera parameters, illumination, shape, and albedo in an alternating fashion. Results are demonstrated on videos of hand-held objects moving in front of a fixed light and camera.

algorithm

problem

algorithm

cues

former

latter

cues

algorithm

computing optical flow, shape, motion, lighting, and albedo

dense reconstruction of both textured and texture-less surfaces

image sequence

rigidly-moving Lambertian object

motion

multi-view stereo

photo-metric stereo

videos of hand-held objects

distant illumination

spatial and temporal intensity variation

flow

surface orientation

estimating affine camera parameters, illumination, shape, and albedo

algorithm

computing optical flow, shape, motion, lighting, and albedo

image sequence

algorithm

spatial and temporal intensity variation

algorithm

former

flow

latter

surface orientation

cues

dense reconstruction of both textured and texture-less surfaces

estimating affine camera parameters, illumination, shape, and albedo

algorithm

rigidly-moving Lambertian object

image sequence

distant illumination

rigidly-moving Lambertian object

motion

multi-view stereo

multi-view stereo

photo-metric stereo

former

latter

former

cues

latter

cues


Input: Past work of generating referring expressions mainly utilized attributes of objects and binary relations between objects. However, such an approach does not work well when there is no distinctive attribute among objects. To overcome this limitation, this paper proposes a method utilizing the perceptual groups of objects and n-ary relations among them. The key is to identify groups of objects that are naturally recognized by humans. We conducted psychological experiments with 42 subjects to collect referring expressions in such situations, and built a generation algorithm based on the results. The evaluation using another 23 subjects showed that the proposed method could effectively generate proper referring expressions.

referring expressions

binary relations

n-ary relations

referring expressions

referring expressions

generation algorithm

method


Input: An entity-oriented approach to restricted-domain parsing is proposed. In this approach, the definitions of the structure and surface representation of domain entities are grouped together. Like semantic grammar, this allows easy exploitation of limited domain semantics. In addition, it facilitates fragmentary recognition and the use of multiple parsing strategies, and so is particularly useful for robust recognition of extra-grammatical input. Several advantages from the point of view of language definition are also noted. Representative samples from an entity-oriented language definition are presented, along with a control structure for an entity-oriented parser, some parsing strategies that use the control structure, and worked examples of parses. A parser incorporating the control structure and the parsing strategies is currently under implementation.

entity-oriented approach

semantic grammar

multiple parsing strategies

entity-oriented parser

parsing strategies

parser

parsing strategies

restricted-domain parsing

fragmentary recognition

approach

this

it

structure and surface representation of domain entities

limited domain semantics

recognition of extra-grammatical input

entity-oriented language definition

control structure

control structure

control structure

entity-oriented approach

restricted-domain parsing

this

limited domain semantics

it

fragmentary recognition

it

multiple parsing strategies

multiple parsing strategies

recognition of extra-grammatical input

control structure

entity-oriented parser

control structure

parsing strategies

control structure

parser


Input: This paper summarizes the formalism of Category Cooccurrence Restrictions -LRB- CCRs -RRB- and describes two parsing algorithms that interpret it. CCRs are Boolean conditions on the cooccurrence of categories in local trees which allow the statement of generalizations which can not be captured in other current syntax formalisms. The use of CCRs leads to syntactic descriptions formulated entirely with restrictive statements. The paper shows how conventional algorithms for the analysis of context free languages can be adapted to the CCR formalism. Special attention is given to the part of the parser that checks the fulfillment of logical well-formedness conditions on trees.

formalism of Category Cooccurrence Restrictions -LRB- CCRs -RRB-

CCR formalism

Category Cooccurrence Restrictions -LRB- CCRs -RRB-

CCRs

Boolean conditions

local trees

statement of generalizations

CCRs

syntactic descriptions

restrictive statements

logical well-formedness conditions

trees

parsing algorithms

syntax formalisms

parser

it

algorithms

context free languages

parsing algorithms

it

algorithms

CCR formalism

context free languages

algorithms

restrictive statements

syntactic descriptions

logical well-formedness conditions

trees


Input: We present a text mining method for finding synonymous expressions based on the distributional hypothesis in a set of coherent corpora. This paper proposes a new methodology to improve the accuracy of a term aggregation system using each author's text as a coherent corpus. Our approach is based on the idea that one person tends to use one expression for one meaning. According to our assumption, most of the words with similar context features in each author's corpus tend not to be synonymous expressions. Our proposed method improves the accuracy of our term aggregation system, showing that our approach is successful.

text mining method

term aggregation system

term aggregation system

synonymous expressions

distributional hypothesis

similar context features

synonymous expressions

methodology

approach

method

approach

accuracy

accuracy

text mining method

synonymous expressions

distributional hypothesis

text mining method

accuracy

term aggregation system

term aggregation system

methodology

accuracy

term aggregation system

term aggregation system

method


Input: In this work, we present a technique for robust estimation, which by explicitly incorporating the inherent uncertainty of the estimation procedure, results in a more efficient robust estimation algorithm. In addition, we build on recent work in randomized model verification, and use this to characterize the `non-randomness' of a solution. The combination of these two strategies results in a robust estimation procedure that provides a significant speed-up over existing RANSAC techniques, while requiring no prior information to guide the sampling process. In particular, our algorithm requires, on average, 3-10 times fewer samples than standard RANSAC, which is in close agreement with theoretical predictions. The efficiency of the algorithm is demonstrated on a selection of geometric estimation problems.

technique

this

strategies

algorithm

algorithm

robust estimation

randomized model verification

geometric estimation problems

inherent uncertainty of the estimation procedure

prior information

sampling process

theoretical predictions

efficient robust estimation algorithm

robust estimation procedure

RANSAC techniques

RANSAC

technique

robust estimation

technique

efficient robust estimation algorithm

inherent uncertainty of the estimation procedure

technique

strategies

robust estimation procedure

RANSAC techniques

robust estimation procedure

algorithm

RANSAC

geometric estimation problems

algorithm


Input: An attempt has been made to use an Augmented Transition Network as a procedural dialog model. The development of such a model appears to be important in several respects: as a device to represent and to use different dialog schemata proposed in empirical conversation analysis; as a device to represent and to use models of verbal interaction; as a device combining knowledge about dialog schemata and about verbal interaction with knowledge about task-oriented and goal-directed dialogs. A standard ATN should be further developed in order to account for the verbal interactions of task-oriented dialogs.

Augmented Transition Network

dialog model

conversation analysis

ATN

model

device

device

models

device

dialog schemata

verbal interaction

dialog schemata

verbal interaction

verbal interactions

task-oriented and goal-directed dialogs

task-oriented dialogs

Augmented Transition Network

dialog model

dialog schemata

device

dialog schemata

conversation analysis

models

device

models

verbal interaction

ATN

verbal interactions

dialog schemata

verbal interaction

verbal interactions

task-oriented dialogs


Input: We present a practically unsupervised learning method to produce single-snippet answers to definition questions in question answering systems that supplement Web search engines. The method exploits on-line encyclopedias and dictionaries to generate automatically an arbitrarily large number of positive and negative definition examples, which are then used to train an svm to separate the two classes. We show experimentally that the proposed method is viable, that it outperforms the alternative of training the system on questions and news articles from trec, and that it helps the search engine handle definition questions significantly better.

unsupervised learning method

question answering systems

Web search engines

svm

search engine

single-snippet answers

method

method

it

alternative

system

it

on-line encyclopedias and dictionaries

positive and negative definition examples

news articles

trec

unsupervised learning method

single-snippet answers

question answering systems

Web search engines

method

on-line encyclopedias and dictionaries

on-line encyclopedias and dictionaries

positive and negative definition examples

positive and negative definition examples

svm

news articles

system

it

search engine

it

alternative

news articles

trec


Input: We revisit the classical decision-theoretic problem of weighted expert voting from a statistical learning perspective. In particular, we examine the consistency -LRB- both asymptotic and finitary -RRB- of the optimal Nitzan-Paroush weighted majority and related rules. In the case of known expert competence levels, we give sharp error estimates for the optimal rule. When the competence levels are unknown, they must be empirically estimated. We provide frequentist and Bayesian analyses for this situation. Some of our proof techniques are non-standard and may be of independent interest. The bounds we derive are nearly optimal, and several challenging open problems are posed. Experimental results are provided to illustrate the theory.

classical decision-theoretic problem of weighted expert voting

statistical learning perspective

sharp error estimates

Bayesian analyses

Nitzan-Paroush weighted majority

expert competence levels

optimal rule

competence levels

statistical learning perspective

classical decision-theoretic problem of weighted expert voting

sharp error estimates

optimal rule


Input: We analyze a reweighted version of the Kikuchi approximation for estimating the log partition function of a product distribution defined over a region graph. We establish sufficient conditions for the concavity of our reweighted objective function in terms of weight assignments in the Kikuchi expansion, and show that a reweighted version of the sum product algorithm applied to the Kikuchi region graph will produce global optima of the Kikuchi approximation whenever the algorithm converges. When the region graph has two layers, corresponding to a Bethe approximation, we show that our sufficient conditions for concavity are also necessary. Finally, we provide an explicit characterization of the polytope of concavity in terms of the cycle structure of the region graph. We conclude with simulations that demonstrate the advantages of the reweighted Kikuchi approach.

reweighted version of the Kikuchi approximation

Kikuchi approximation

reweighted version of the sum product algorithm

Kikuchi approximation

Bethe approximation

reweighted Kikuchi approach

log partition function of a product distribution

region graph

concavity

reweighted objective function

weight assignments

Kikuchi expansion

Kikuchi region graph

global optima

region graph

concavity

concavity

cycle structure

region graph

algorithm

reweighted version of the Kikuchi approximation

log partition function of a product distribution

reweighted version of the sum product algorithm

Kikuchi region graph

log partition function of a product distribution

region graph

concavity

reweighted objective function

global optima

Kikuchi approximation

cycle structure

region graph


Input: We apply a decision tree based approach to pronoun resolution in spoken dialogue. Our system deals with pronouns with NP-and non-NP-antecedents. We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features. We evaluate the system on twenty Switchboard dialogues and show that it compares well to Byron's -LRB- 2002 -RRB- manually tuned system.

decision tree based approach

Byron's -LRB- 2002 -RRB- manually tuned system

pronoun resolution

spoken dialogue

pronoun resolution

spoken dialogue

system

system

it

pronouns

NP-and non-NP-antecedents

features

features

Switchboard dialogues

decision tree based approach

pronoun resolution

pronoun resolution

spoken dialogue

system

pronouns

NP-and non-NP-antecedents

pronouns

features

pronoun resolution

pronoun resolution

spoken dialogue

Switchboard dialogues

system

it

Byron's -LRB- 2002 -RRB- manually tuned system


Input: We present a new approach for building an efficient and robust classifier for the two class problem, that localizes objects that may appear in the image under different orien-tations. In contrast to other works that address this problem using multiple classifiers, each one specialized for a specific orientation, we propose a simple two-step approach with an estimation stage and a classification stage. The estimator yields an initial set of potential object poses that are then validated by the classifier. This methodology allows reducing the time complexity of the algorithm while classification results remain high. The classifier we use in both stages is based on a boosted combination of Random Ferns over local histograms of oriented gradients -LRB- HOGs -RRB-, which we compute during a pre-processing step. Both the use of supervised learning and working on the gradient space makes our approach robust while being efficient at run-time. We show these properties by thorough testing on standard databases and on a new database made of motorbikes under planar rotations, and with challenging conditions such as cluttered backgrounds, changing illumination conditions and partial occlusions.

approach

problem

approach

algorithm

approach

database

conditions

classifier

classifiers

estimation stage

classification stage

estimator

classifier

classifier

boosted combination of Random Ferns

pre-processing step

supervised learning

class problem

classification

image

motorbikes under planar rotations

orien-tations

object poses

local histograms of oriented gradients -LRB- HOGs -RRB-

gradient space

cluttered backgrounds

changing illumination conditions

partial occlusions

time complexity

approach

classifier

classifier

class problem

classifier

object poses

boosted combination of Random Ferns

classifier

pre-processing step

local histograms of oriented gradients -LRB- HOGs -RRB-

supervised learning

approach

gradient space

approach

estimation stage

approach

classification stage

approach

estimation stage

classification stage

cluttered backgrounds

changing illumination conditions

changing illumination conditions

partial occlusions

time complexity

algorithm

local histograms of oriented gradients -LRB- HOGs -RRB-

boosted combination of Random Ferns

motorbikes under planar rotations

database

conditions

database

cluttered backgrounds

conditions

changing illumination conditions

conditions

partial occlusions

conditions


Input: The following describes recent work on the Lincoln CSR system. Some new variations in semiphone modeling have been tested. A very simple improved duration model has reduced the error rate by about 10% in both triphone and semiphone systems. A new training strategy has been tested which, by itself, did not provide useful improvements but suggests that improvements can be obtained by a related rapid adaptation technique. Finally, the recognizer has been modified to use bigram back-off language models. The system was then transferred from the RM task to the ATIS CSR task and a limited number of development tests performed. Evaluation test results are presented for both the RM and ATIS CSR tasks.

Lincoln CSR system

semiphone modeling

duration model

triphone and semiphone systems

training strategy

rapid adaptation technique

recognizer

bigram back-off language models

error rate

system

RM task

ATIS CSR task

RM and ATIS CSR tasks

duration model

triphone and semiphone systems

rapid adaptation technique

training strategy

bigram back-off language models

recognizer

system

RM task

system

ATIS CSR task

error rate

triphone and semiphone systems

RM task

ATIS CSR task


Input: A new approach for Interactive Machine Translation where the author interacts during the creation or the modification of the document is proposed. The explanation of an ambiguity or an error for the purposes of correction does not use any concepts of the underlying linguistic theory: it is a reformulation of the erroneous or ambiguous sentence. The interaction is limited to the analysis step of the translation process. This paper presents a new interactive disambiguation scheme based on the paraphrasing of a parser's multiple output. Some examples of paraphrasing ambiguous sentences are presented.

approach

Interactive Machine Translation

linguistic theory

translation process

interactive disambiguation scheme

paraphrasing

approach

Interactive Machine Translation

paraphrasing

interactive disambiguation scheme


Input: We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation. This method requires a source-language dependency parser, target language word segmentation and an unsupervised word alignment component. We align a parallel corpus, project the source dependency parse onto the target sentence, extract dependency treelet translation pairs, and train a tree-based ordering model. We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser.

approach

method

approach

statistical machine translation

phrasal translation

syntactic information

source dependency parse

dependency treelet translation pairs

linguistic generality

source-language dependency parser

target language word segmentation

unsupervised word alignment component

tree-based ordering model

decoder

tree-based models

SMT models

phrasal SMT

parser

parallel corpus

approach

statistical machine translation

source-language dependency parser

method

target language word segmentation

method

unsupervised word alignment component

method

tree-based models

approach

SMT models

approach

phrasal SMT

parser

syntactic information

approach

phrasal translation

approach

syntactic information

phrasal translation

source-language dependency parser

target language word segmentation

target language word segmentation

unsupervised word alignment component

tree-based models

SMT models

phrasal SMT

linguistic generality

linguistic generality

parser


Input: Video provides not only rich visual cues such as motion and appearance, but also much less explored long-range temporal interactions among objects. We aim to capture such interactions and to construct a powerful intermediate-level video representation for subsequent recognition. Motivated by this goal, we seek to obtain spatio-temporal over-segmentation of a video into regions that respect object boundaries and, at the same time, associate object pix-els over many video frames. The contributions of this paper are twofold. First, we develop an efficient spatio-temporal video segmentation algorithm, which naturally incorporates long-range motion cues from the past and future frames in the form of clusters of point tracks with coherent motion. Second, we devise a new track clustering cost function that includes occlusion reasoning, in the form of depth ordering constraints, as well as motion similarity along the tracks. We evaluate the proposed approach on a challenging set of video sequences of office scenes from feature length movies.

Video

video sequences of office scenes

movies

visual cues

motion

appearance

long-range temporal interactions

spatio-temporal over-segmentation

object boundaries

long-range motion cues

clusters of point tracks

occlusion reasoning

depth ordering constraints

motion similarity

interactions

approach

intermediate-level video representation

spatio-temporal video segmentation algorithm

track clustering cost function

recognition

visual cues

Video

depth ordering constraints

occlusion reasoning

motion

visual cues

appearance

visual cues

motion

appearance

intermediate-level video representation

recognition

long-range motion cues

spatio-temporal video segmentation algorithm

clusters of point tracks

long-range motion cues

occlusion reasoning

track clustering cost function

motion similarity

track clustering cost function

video sequences of office scenes

approach


Input: In this paper, we introduce KAZE features, a novel multiscale 2D feature detection and description algorithm in nonlinear scale spaces. Previous approaches detect and describe features at different scale levels by building or approximating the Gaussian scale space of an image. However, Gaussian blurring does not respect the natural boundaries of objects and smoothes to the same degree both details and noise, reducing localization accuracy and distinctiveness. In contrast, we detect and describe 2D features in a nonlinear scale space by means of nonlinear diffusion filtering. In this way, we can make blurring locally adaptive to the image data, reducing noise but retaining object boundaries, obtaining superior localization accuracy and distinctiviness. The nonlinear scale space is built using efficient Additive Operator Splitting -LRB- AOS -RRB- techniques and variable con-ductance diffusion. We present an extensive evaluation on benchmark datasets and a practical matching application on deformable surfaces. Even though our features are somewhat more expensive to compute than SURF due to the construction of the nonlinear scale space, but comparable to SIFT, our results reveal a step forward in performance both in detection and description against previous state-of-the-art methods.

KAZE features

multiscale 2D feature detection and description algorithm

2D features

nonlinear diffusion filtering

Additive Operator Splitting -LRB- AOS -RRB- techniques

variable con-ductance diffusion

SURF

SIFT

nonlinear scale spaces

Gaussian scale space

Gaussian blurring

boundaries of objects

nonlinear scale space

object boundaries

nonlinear scale space

nonlinear scale space

localization accuracy and distinctiveness

localization accuracy and distinctiviness

image data

benchmark datasets

matching application on deformable surfaces

detection

description

features

results

state-of-the-art methods

KAZE features

multiscale 2D feature detection and description algorithm

nonlinear scale spaces

multiscale 2D feature detection and description algorithm

nonlinear scale space

2D features

nonlinear diffusion filtering

2D features

Additive Operator Splitting -LRB- AOS -RRB- techniques

nonlinear scale space

variable con-ductance diffusion

nonlinear scale space

Additive Operator Splitting -LRB- AOS -RRB- techniques

variable con-ductance diffusion

detection

description

features

SURF

features

SIFT

results

state-of-the-art methods

detection

results

detection

state-of-the-art methods

description

results

description

state-of-the-art methods


Input: Semantic Web documents that encode facts about entities on the Web have been growing rapidly in size and evolving over time. Creating summaries on lengthy Semantic Web documents for quick identification of the corresponding entity has been of great contemporary interest. In this paper, we explore automatic summa-rization techniques that characterize and enable identification of an entity and create summaries that are human friendly. Specifically, we highlight the importance of diversified -LRB- faceted -RRB- summaries by combining three dimensions: diversity, uniqueness, and popularity. Our novel diversity-aware entity summarization approach mimics human conceptual clustering techniques to group facts, and picks representative facts from each group to form concise -LRB- i.e., short -RRB- and comprehensive -LRB- i.e., improved coverage through diversity -RRB- summaries. We evaluate our approach against the state-of-the-art techniques and show that our work improves both the quality and the efficiency of entity summarization.

Semantic Web documents

lengthy Semantic Web documents

Creating summaries

identification of the corresponding entity

entity summarization

automatic summa-rization techniques

diversity-aware entity summarization approach

human conceptual clustering techniques

diversified -LRB- faceted -RRB- summaries

diversity

uniqueness

popularity

approach

state-of-the-art techniques

quality

efficiency

Creating summaries

identification of the corresponding entity

lengthy Semantic Web documents

Creating summaries

human conceptual clustering techniques

diversity-aware entity summarization approach

approach

entity summarization

state-of-the-art techniques

entity summarization

diversity

diversified -LRB- faceted -RRB- summaries

uniqueness

diversified -LRB- faceted -RRB- summaries

popularity

diversified -LRB- faceted -RRB- summaries

diversity

uniqueness

uniqueness

popularity

state-of-the-art techniques

approach

quality

entity summarization

efficiency

entity summarization


Input: We present a framework for the fast computation of lexical affinity models. The framework is composed of a novel algorithm to efficiently compute the co-occurrence distribution between pairs of terms, an independence model, and a parametric affinity model. In comparison with previous models, which either use arbitrary windows to compute similarity between words or use lexical affinity to create sequential models, in this paper we focus on models intended to capture the co-occurrence patterns of any pair of words or phrases at any distance in the corpus. The framework is flexible, allowing fast adaptation to applications and it is scalable. We apply it in combination with a terabyte corpus to answer natural language tests, achieving encouraging results.

framework

framework

algorithm

models

models

framework

it

fast computation of lexical affinity models

natural language tests

co-occurrence distribution

similarity

lexical affinity

co-occurrence patterns

independence model

parametric affinity model

sequential models

terabyte corpus

framework

fast computation of lexical affinity models

algorithm

co-occurrence distribution

lexical affinity

sequential models

models

co-occurrence patterns

it

natural language tests

algorithm

framework

independence model

framework

parametric affinity model

framework

algorithm

independence model

independence model

parametric affinity model

models

models

terabyte corpus

it


Input: This paper introduces a system for categorizing unknown words. The system is based on a multi-component architecture where each component is responsible for identifying one class of unknown words. The focus of this paper is the components that identify names and spelling errors. Each component uses a decision tree architecture to combine multiple types of evidence about the unknown word. The system is evaluated using data from live closed captions-a genre replete with a wide variety of unknown words.

system

system

component

components

component

system

categorizing unknown words

multi-component architecture

decision tree architecture

unknown words

names

spelling errors

unknown word

unknown words

live closed captions

system

categorizing unknown words

multi-component architecture

system

component

unknown words

components

names

components

spelling errors

decision tree architecture

component

component

multi-component architecture

names

spelling errors

live closed captions

system


Input: At MIT Lincoln Laboratory, we have been developing a Korean-to-English machine translation system CCLINC -LRB- Common Coalition Language System at Lincoln Laboratory -RRB-. The CCLINC Korean-to-English translation system consists of two core modules, language understanding and generation modules mediated by a language neutral meaning representation called a semantic frame. The key features of the system include: -LRB- i -RRB- Robust efficient parsing of Korean -LRB- a verb final language with overt case markers, relatively free word order, and frequent omissions of arguments -RRB-. -LRB- ii -RRB- High quality translation via word sense disambiguation and accurate word order generation of the target language. -LRB- iii -RRB- Rapid system development and porting to new domains via knowledge-based automated acquisition of grammars. Having been trained on Korean newspaper articles on missiles and chemical biological warfare, the system produces the translation output sufficient for content understanding of the original document.

Korean-to-English machine translation system

CCLINC -LRB- Common Coalition Language System at Lincoln Laboratory -RRB-

CCLINC Korean-to-English translation system

language understanding and generation modules

language neutral meaning representation

word sense disambiguation

knowledge-based automated acquisition of grammars

core modules

system

system

semantic frame

overt case markers

parsing of Korean

translation

word order generation

Korean

verb final language

Korean newspaper articles

missiles and chemical biological warfare

CCLINC -LRB- Common Coalition Language System at Lincoln Laboratory -RRB-

Korean-to-English machine translation system

semantic frame

language neutral meaning representation

Korean

verb final language

core modules

CCLINC Korean-to-English translation system

language neutral meaning representation

language understanding and generation modules

word sense disambiguation

translation

word order generation

translation

Korean newspaper articles

system

overt case markers

verb final language

missiles and chemical biological warfare

Korean newspaper articles

word sense disambiguation

word order generation


Input: The JAVELIN system integrates a flexible, planning-based architecture with a variety of language processing modules to provide an open-domain question answering capability on free text. The demonstration will focus on how JAVELIN processes questions and retrieves the most likely answer candidates from the given text corpus. The operation of the system will be explained in depth through browsing the repository of data objects created by the system during each question answering session.

JAVELIN system

planning-based architecture

language processing modules

JAVELIN

open-domain question answering capability

question answering

system

system

JAVELIN system

open-domain question answering capability

planning-based architecture

JAVELIN system

language processing modules

JAVELIN system

language processing modules

planning-based architecture


Input: We present the first application of the head-driven statistical parsing model of Collins -LRB- 1999 -RRB- as a simultaneous language model and parser for large-vocabulary speech recognition. The model is adapted to an online left to right chart-parser for word lattices, integrating acoustic, n-gram, and parser probabilities. The parser uses structural and lexical dependencies not considered by n-gram models, conditioning recognition on more linguistically-grounded relationships. Experiments on the Wall Street Journal treebank and lattice corpora show word error rates competitive with the standard n-gram language model while extracting additional structural information useful for speech understanding.

head-driven statistical parsing model

simultaneous language model

parser

online left to right chart-parser

parser

n-gram models

n-gram language model

large-vocabulary speech recognition

speech understanding

model

word lattices

acoustic, n-gram, and parser probabilities

structural and lexical dependencies

structural information

Wall Street Journal treebank

lattice corpora

word error rates

head-driven statistical parsing model

simultaneous language model

head-driven statistical parsing model

parser

simultaneous language model

large-vocabulary speech recognition

parser

large-vocabulary speech recognition

model

online left to right chart-parser

online left to right chart-parser

word lattices

structural and lexical dependencies

parser

structural information

speech understanding

simultaneous language model

parser

Wall Street Journal treebank

lattice corpora

acoustic, n-gram, and parser probabilities

online left to right chart-parser

Wall Street Journal treebank

n-gram language model

lattice corpora

n-gram language model

word error rates

n-gram language model


Input: Image composition -LRB- or mosaicing -RRB- has attracted a growing attention in recent years as one of the main elements in video analysis and representation. In this paper we deal with the problem of global alignment and super-resolution. We also propose to evaluate the quality of the resulting mosaic by measuring the amount of blurring. Global registration is achieved by combining a graph-based technique -- that exploits the topological structure of the sequence induced by the spatial overlap -- with a bundle adjustment which uses only the homographies computed in the previous steps. Experimental comparison with other techniques shows the effectiveness of our approach.

Image composition -LRB- or mosaicing -RRB-

video analysis and representation

global alignment

super-resolution

mosaic

Global registration

amount of blurring

graph-based technique

bundle adjustment

topological structure

spatial overlap

homographies

techniques

approach

Image composition -LRB- or mosaicing -RRB-

video analysis and representation

global alignment

super-resolution

graph-based technique

bundle adjustment

amount of blurring

mosaic

graph-based technique

Global registration

graph-based technique

topological structure

bundle adjustment

Global registration

homographies

bundle adjustment

approach

techniques


Input: The project presented here is a part of a long term research program aiming at a full lexicon grammar for Polish -LRB- SyntLex -RRB-. The main of this project is computer-assisted acquisition and morpho-syntactic description of verb-noun collocations in Polish. We present methodology and resources obtained in three main project phases which are: dictionary-based acquisition of collocation lexicon, feasibility study for corpus-based lexicon enlargement phase, corpus-based lexicon enlargement and collocation description. In this paper we focus on the results of the third phase. The presented here corpus-based approach permitted us to triple the size the verb-noun collocation dictionary for Polish. In the paper we describe the SyntLex Dictionary of Collocations and announce some future research intended to be a separate project continuation.

lexicon grammar for Polish -LRB- SyntLex -RRB-

corpus-based approach

computer-assisted acquisition and morpho-syntactic description of verb-noun collocations

dictionary-based acquisition of collocation lexicon

feasibility study

corpus-based lexicon enlargement phase

corpus-based lexicon enlargement and collocation description

Polish

verb-noun collocation dictionary

Polish

SyntLex Dictionary of Collocations

phases

Polish

computer-assisted acquisition and morpho-syntactic description of verb-noun collocations

feasibility study

corpus-based lexicon enlargement phase

corpus-based approach

verb-noun collocation dictionary

dictionary-based acquisition of collocation lexicon

phases

feasibility study

phases

corpus-based lexicon enlargement and collocation description

phases

dictionary-based acquisition of collocation lexicon

feasibility study

corpus-based lexicon enlargement and collocation description

feasibility study

Polish

verb-noun collocation dictionary


Input: Along with the increasing requirements, the hash-tag recommendation task for microblogs has been receiving considerable attention in recent years. Various researchers have studied the problem from different aspects. However, most of these methods usually need handcrafted features. Motivated by the successful use of convolutional neural networks -LRB- CNNs -RRB- for many natural language processing tasks, in this paper, we adopt CNNs to perform the hashtag recommendation problem. To incorporate the trigger words whose effectiveness have been experimentally evaluated in several previous works, we propose a novel architecture with an attention mechanism. The results of experiments on the data collected from a real world microblogging service demonstrated that the proposed model outperforms state-of-the-art methods. By incorporating trigger words into the consideration, the relative improvement of the proposed method over the state-of-the-art method is around 9.4% in the F1-score.

hash-tag recommendation task

natural language processing tasks

hashtag recommendation problem

microblogs

handcrafted features

trigger words

attention mechanism

trigger words

convolutional neural networks -LRB- CNNs -RRB-

CNNs

architecture

data

model

state-of-the-art methods

method

state-of-the-art method

hash-tag recommendation task

microblogs

convolutional neural networks -LRB- CNNs -RRB-

natural language processing tasks

architecture

trigger words

attention mechanism

architecture

data

model

state-of-the-art method

model

state-of-the-art methods

method

state-of-the-art method


Input: In this paper, we improve an unsupervised learning method using the Expectation-Maximization -LRB- EM -RRB- algorithm proposed by Nigam et al. for text classification problems in order to apply it to word sense disambiguation -LRB- WSD -RRB- problems. The improved method stops the EM algorithm at the optimum iteration number. To estimate that number, we propose two methods. In experiments, we solved 50 noun WSD problems in the Japanese Dictionary Task in SENSEVAL2. The score of our method is a match for the best public score of this task. Furthermore, our methods were confirmed to be effective also for verb WSD problems.

unsupervised learning method

Expectation-Maximization -LRB- EM -RRB- algorithm

EM algorithm

text classification problems

word sense disambiguation -LRB- WSD -RRB- problems

noun WSD problems

Japanese Dictionary Task

verb WSD problems

it

method

number

method

task

methods

optimum iteration number

Expectation-Maximization -LRB- EM -RRB- algorithm

unsupervised learning method

Expectation-Maximization -LRB- EM -RRB- algorithm

text classification problems

it

word sense disambiguation -LRB- WSD -RRB- problems

methods

verb WSD problems

Japanese Dictionary Task


Input: Dividing sentences in chunks of words is a useful preprocessing step for parsing, information extraction and information retrieval. -LRB- Ramshaw and Marcus, 1995 -RRB- have introduced a ``convenient'' data representation for chunking by converting it to a tagging task. In this paper we will examine seven different data representations for the problem of recognizing noun phrase chunks. We will show that the data representation choice has a minor influence on chunking performance. However, equipped with the most suitable data representation, our memory-based learning chunker was able to improve the best published chunking results for a standard data set.

Dividing sentences in chunks of words

parsing

information extraction

information retrieval

chunking

tagging task

recognizing noun phrase chunks

chunking

chunking

data representation

data representations

data representation

data representation

memory-based learning chunker

it

data set

Dividing sentences in chunks of words

parsing

Dividing sentences in chunks of words

information extraction

Dividing sentences in chunks of words

information retrieval

data representation

chunking

data representations

recognizing noun phrase chunks

data representation

memory-based learning chunker

parsing

information extraction

information extraction

information retrieval

data set

memory-based learning chunker


Input: In this paper we describe and evaluate a Question Answering system that goes beyond answering factoid questions. We focus on FAQ-like questions and answers, and build our system around a noisy-channel architecture which exploits both a language model for answers and a transformation model for answer/question terms, trained on a corpus of 1 million question/answer pairs collected from the Web.

Question Answering system

noisy-channel architecture

language model

transformation model

FAQ-like questions and answers

Web

system

system

FAQ-like questions and answers

noisy-channel architecture

system

noisy-channel architecture

language model

noisy-channel architecture

transformation model


Input: In this paper we evaluate four objective measures of speech with regards to intelligibility prediction of synthesized speech in diverse noisy situations. We evaluated three intel-ligibility measures, the Dau measure, the glimpse proportion and the Speech Intelligibility Index -LRB- SII -RRB- and a quality measure, the Perceptual Evaluation of Speech Quality -LRB- PESQ -RRB-. For the generation of synthesized speech we used a state of the art HMM-based speech synthesis system. The noisy conditions comprised four additive noises. The measures were compared with subjective intelligibility scores obtained in listening tests. The results show the Dau and the glimpse measures to be the best predictors of intelligibility, with correlations of around 0.83 to subjective scores. All measures gave less accurate predictions of intelligibility for synthetic speech than have previously been found for natural speech; in particular the SII measure. In additional experiments, we processed the synthesized speech by an ideal binary mask before adding noise. The Glimpse measure gave the most accurate intelligibility predictions in this situation.

measures of speech

intel-ligibility measures

Dau measure

glimpse proportion

Speech Intelligibility Index -LRB- SII -RRB-

quality measure

Perceptual Evaluation of Speech Quality -LRB- PESQ -RRB-

subjective intelligibility scores

Dau

glimpse measures

correlations

SII measure

Glimpse measure

intelligibility prediction

generation of synthesized speech

predictions of intelligibility

intelligibility predictions

synthesized speech

synthetic speech

natural speech

synthesized speech

diverse noisy situations

noisy conditions

additive noises

subjective scores

HMM-based speech synthesis system

predictors of intelligibility

ideal binary mask

measures

measures

measures of speech

intelligibility prediction

correlations

Dau

correlations

glimpse measures

measures

predictions of intelligibility

synthesized speech

intelligibility prediction

HMM-based speech synthesis system

generation of synthesized speech

synthetic speech

predictions of intelligibility

ideal binary mask

synthesized speech

Glimpse measure

intelligibility predictions

diverse noisy situations

synthesized speech

intel-ligibility measures

quality measure

Dau measure

glimpse proportion

glimpse proportion

Speech Intelligibility Index -LRB- SII -RRB-

Dau

glimpse measures

Dau measure

intel-ligibility measures

glimpse proportion

intel-ligibility measures

Speech Intelligibility Index -LRB- SII -RRB-

intel-ligibility measures

Perceptual Evaluation of Speech Quality -LRB- PESQ -RRB-

quality measure

Dau

predictors of intelligibility

glimpse measures

predictors of intelligibility

SII measure

measures

additive noises

noisy conditions

measures

subjective intelligibility scores

Dau

subjective scores

glimpse measures

subjective scores

synthetic speech

natural speech


Input: A'' graphics for vision'' approach is proposed to address the problem of reconstruction from a large and imperfect data set: reconstruction on demand by tensor voting, or ROD-TV. ROD-TV simultaneously delivers good efficiency and robust-ness, by adapting to a continuum of primitive connectivity, view dependence, and levels of detail -LRB- LOD -RRB-. Locally inferred surface elements are robust to noise and better capture local shapes. By inferring per-vertex normals at sub-voxel precision on the fly, we can achieve interpolative shading. Since these missing details can be recovered at the current level of detail, our result is not upper bounded by the scanning resolution. By relaxing the mesh connectivity requirement, we extend ROD-TV and propose a simple but effective multiscale feature extraction algorithm. ROD-TV consists of a hierarchical data structure that encodes different levels of detail. The local reconstruction algorithm is tensor voting. It is applied on demand to the visible subset of data at a desired level of detail, by traversing the data hierarchy and collecting tensorial support in a neighborhood. We compare our approach and present encouraging results.

'' graphics for vision'' approach

tensor voting

ROD-TV

ROD-TV

ROD-TV

multiscale feature extraction algorithm

ROD-TV

hierarchical data structure

local reconstruction algorithm

tensor voting

traversing the data hierarchy

collecting tensorial support

reconstruction

reconstruction

interpolative shading

large and imperfect data set

efficiency

robust-ness

sub-voxel precision

primitive connectivity

view dependence

levels of detail -LRB- LOD -RRB-

Locally inferred surface elements

noise

local shapes

per-vertex normals

scanning resolution

mesh connectivity requirement

It

approach

'' graphics for vision'' approach

reconstruction

large and imperfect data set

reconstruction

tensor voting

reconstruction

ROD-TV

reconstruction

Locally inferred surface elements

local shapes

per-vertex normals

interpolative shading

mesh connectivity requirement

multiscale feature extraction algorithm

ROD-TV

multiscale feature extraction algorithm

traversing the data hierarchy

It

collecting tensorial support

It

tensor voting

ROD-TV

robust-ness

efficiency

view dependence

primitive connectivity

levels of detail -LRB- LOD -RRB-

view dependence

traversing the data hierarchy

collecting tensorial support

efficiency

ROD-TV

robust-ness

ROD-TV

sub-voxel precision

per-vertex normals

hierarchical data structure

ROD-TV

tensor voting

local reconstruction algorithm


Input: Both rhetorical structure and punctuation have been helpful in discourse processing. Based on a corpus annotation project, this paper reports the discursive usage of 6 Chinese punctuation marks in news commentary texts: Colon, Dash, Ellipsis, Exclamation Mark, Question Mark, and Semicolon. The rhetorical patterns of these marks are compared against patterns around cue phrases in general. Results show that these Chinese punctuation marks, though fewer in number than cue phrases, are easy to identify, have strong correlation with certain relations, and can be used as distinctive indicators of nuclearity in Chinese texts.

rhetorical structure

punctuation

Chinese punctuation marks

Colon

Dash

Ellipsis

Exclamation Mark

Question Mark

Semicolon

rhetorical patterns

patterns around cue phrases

Chinese punctuation marks

cue phrases

indicators of nuclearity

discourse processing

corpus annotation project

discursive usage of 6 Chinese punctuation marks

news commentary texts

Chinese texts

marks

rhetorical structure

punctuation

Colon

Dash

Dash

Ellipsis

Ellipsis

Exclamation Mark

Exclamation Mark

Question Mark

Question Mark

Semicolon

rhetorical structure

discourse processing

punctuation

discourse processing

Chinese punctuation marks

indicators of nuclearity

Chinese punctuation marks

news commentary texts

Colon

Chinese punctuation marks

Dash

Chinese punctuation marks

Ellipsis

Chinese punctuation marks

Exclamation Mark

Chinese punctuation marks

Question Mark

Chinese punctuation marks

Semicolon

Chinese punctuation marks

rhetorical patterns

marks

Chinese texts

indicators of nuclearity

rhetorical patterns

patterns around cue phrases

Chinese punctuation marks

cue phrases


Input: The features based on Markov random field -LRB- MRF -RRB- models are usually sensitive to the rotation of image textures. This paper develops an anisotropic circular Gaussian MRF -LRB- ACGMRF -RRB- model for modelling rotated image textures and retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate -LRB- LSE -RRB- method, an approximate least squares estimate -LRB- ALSE -RRB- method is proposed to estimate the parameters of the ACGMRF model. The rotation-invariant features can be obtained from the parameters of the ACGMRF model by the one-dimensional -LRB- 1-D -RRB- discrete Fourier transform -LRB- DFT -RRB-. Significantly improved accuracy can be achieved by applying the rotation-invariant features to classify SAR -LRB- synthetic aperture radar -RRB- sea ice and Brodatz imagery.

features

rotation of image textures

singularity problem

parameters of the ACGMRF model

rotation-invariant features

parameters of the ACGMRF model

rotation-invariant features

SAR -LRB- synthetic aperture radar

Markov random field -LRB- MRF -RRB- models

anisotropic circular Gaussian MRF -LRB- ACGMRF -RRB- model

least squares estimate -LRB- LSE -RRB- method

approximate least squares estimate -LRB- ALSE -RRB- method

ACGMRF model

ACGMRF model

one-dimensional -LRB- 1-D -RRB- discrete Fourier transform -LRB- DFT -RRB-

modelling rotated image textures

retrieving rotation-invariant texture features

accuracy

Markov random field -LRB- MRF -RRB- models

features

anisotropic circular Gaussian MRF -LRB- ACGMRF -RRB- model

modelling rotated image textures

anisotropic circular Gaussian MRF -LRB- ACGMRF -RRB- model

retrieving rotation-invariant texture features

approximate least squares estimate -LRB- ALSE -RRB- method

parameters of the ACGMRF model

parameters of the ACGMRF model

rotation-invariant features

one-dimensional -LRB- 1-D -RRB- discrete Fourier transform -LRB- DFT -RRB-

rotation-invariant features

rotation-invariant features

SAR -LRB- synthetic aperture radar

modelling rotated image textures

retrieving rotation-invariant texture features

singularity problem

least squares estimate -LRB- LSE -RRB- method


Input: Despite much recent progress on accurate semantic role labeling, previous work has largely used independent classifiers, possibly combined with separate label sequence models via Viterbi decoding. This stands in stark contrast to the linguistic observation that a core argument frame is a joint structure, with strong dependencies between arguments. We show how to build a joint model of argument frames, incorporating novel features that model these interactions into discriminative log-linear models. This system achieves an error reduction of 22% on all arguments and 32% on core arguments over a state-of-the art independent classifier for gold-standard parse trees on PropBank.

semantic role labeling

independent classifiers

label sequence models

Viterbi decoding

joint model of argument frames

discriminative log-linear models

independent classifier

core argument frame

features

gold-standard parse trees

system

error reduction

PropBank

independent classifiers

semantic role labeling

Viterbi decoding

label sequence models

independent classifiers

label sequence models

features

discriminative log-linear models

gold-standard parse trees

PropBank

error reduction

system

error reduction

independent classifier

gold-standard parse trees

system

gold-standard parse trees

independent classifier

independent classifier

system


Input: One of the major problems one is faced with when decomposing words into their constituent parts is ambiguity: the generation of multiple analyses for one input word, many of which are implausible. In order to deal with ambiguity, the MORphological PArser MORPA is provided with a probabilistic context-free grammar -LRB- PCFG -RRB-, i.e. it combines a ``conventional'' context-free morphological grammar to filter out ungrammatical segmentations with a probability-based scoring function which determines the likelihood of each successful parse. Consequently, remaining analyses can be ordered along a scale of plausibility. Test performance data will show that a PCFG yields good results in morphological parsing. MORPA is a fully implemented parser developed for use in a text-to-speech conversion system.

ambiguity

ambiguity

ungrammatical segmentations

generation

parse

morphological parsing

text-to-speech conversion system

MORphological PArser MORPA

probabilistic context-free grammar -LRB- PCFG -RRB-

``conventional'' context-free morphological grammar

probability-based scoring function

PCFG

MORPA

parser

it

MORphological PArser MORPA

ambiguity

probabilistic context-free grammar -LRB- PCFG -RRB-

MORphological PArser MORPA

``conventional'' context-free morphological grammar

it

``conventional'' context-free morphological grammar

ungrammatical segmentations

probability-based scoring function

it

probability-based scoring function

parse

PCFG

morphological parsing

MORPA

text-to-speech conversion system

parser

text-to-speech conversion system

probability-based scoring function

``conventional'' context-free morphological grammar

MORPA

parser


Input: This paper describes the framework of a Korean phonological knowledge base system using the unification-based grammar formalism: Korean Phonology Structure Grammar -LRB- KPSG -RRB-. The approach of KPSG provides an explicit development model for constructing a computational phonological system: speech recognition and synthesis system. We show that the proposed approach is more describable than other approaches such as those employing a traditional generative phonological approach.

Korean phonological knowledge base system

phonological system

speech recognition and synthesis system

unification-based grammar formalism

Korean Phonology Structure Grammar -LRB- KPSG -RRB-

KPSG

generative phonological approach

approach

approach

approaches

those

unification-based grammar formalism

Korean phonological knowledge base system

approach

KPSG

KPSG

phonological system

generative phonological approach

those

Korean Phonology Structure Grammar -LRB- KPSG -RRB-

unification-based grammar formalism

approach

approaches


Input: In some auction domains, there is uncertainty regarding the final availability of the goods being auctioned off. For example, a government may auction off spectrum from its public safety network, but it may need this spectrum back in times of emergency. In such a domain, standard combinatorial auctions perform poorly because they lead to violations of individual rationality -LRB- IR -RRB-, even in expectation, and to very low efficiency. In this paper, we study the design of core-selecting payment rules for such domains. Surprisingly, we show that in this new domain, there does not exist a payment rule with is guaranteed to be ex-post core-selecting. However, we show that by designing rules that are'' execution-contingent,'' i.e., by charging payments that are conditioned on the realization of the availability of the goods, we can reduce IR violations. We design two core-selecting rules that always satisfy IR in expectation. To study the performance of our rules we perform a computational Bayes-Nash equilibrium analysis. We show that, in equilibrium, our new rules have better incentives, higher efficiency, and a lower rate of ex-post IR violations than standard core-selecting rules.

auction domains

design of core-selecting payment rules

domain

they

domains

domain

rules

rules

combinatorial auctions

computational Bayes-Nash equilibrium analysis

violations of individual rationality -LRB- IR -RRB-

individual rationality -LRB- IR -RRB-

payment rule

rules

IR violations

core-selecting rules

IR

core-selecting rules

rate of ex-post IR violations

design of core-selecting payment rules

domains

core-selecting rules

IR

computational Bayes-Nash equilibrium analysis

rules

rules

core-selecting rules

rate of ex-post IR violations

rules

rate of ex-post IR violations

core-selecting rules


Input: In this paper, we will describe a search tool for a huge set of ngrams. The tool supports queries with an arbitrary number of wildcards. It takes a fraction of a second for a search, and can provide the fillers of the wildcards. The system runs on a single Linux PC with reasonable size memory -LRB- less than 4GB -RRB- and disk space -LRB- less than 400GB -RRB-. This system can be a very useful tool for linguistic knowledge discovery and other NLP tasks.

search tool

ngrams

memory

disk space

tool

It

system

system

tool

linguistic knowledge discovery

NLP tasks

search tool

ngrams

tool

linguistic knowledge discovery

tool

NLP tasks

linguistic knowledge discovery

NLP tasks


Input: For intelligent interactive systems to communicate with humans in a natural manner, they must have knowledge about the system users. This paper explores the role of user modeling in such systems. It begins with a characterization of what a user model is and how it can be used. The types of information that a user model may be required to keep about a user are then identified and discussed. User models themselves can vary greatly depending on the requirements of the situation and the implementation, so several dimensions along which they can be classified are presented. Since acquiring the knowledge for a user model is a fundamental problem in user modeling, a section is devoted to this topic. Next, the benefits and costs of implementing a user modeling component for a system are weighed in light of several aspects of the interaction requirements that may be imposed by the system. Finally, the current state of research in user modeling is summarized, and future research topics that must be addressed in order to achieve powerful, general user modeling systems are assessed.

intelligent interactive systems

systems

user model

user model

User models

user model

user modeling component

user modeling systems

they

it

they

system

system

user modeling

user modeling

user modeling

user modeling

systems

user modeling component

system

user model

user modeling


Input: Information extraction techniques automatically create structured databases from unstructured data sources, such as the Web or newswire documents. Despite the successes of these systems, accuracy will always be imperfect. For many reasons, it is highly desirable to accurately estimate the confidence the system has in the correctness of each extracted field. The information extraction system we evaluate is based on a linear-chain conditional random field -LRB- CRF -RRB-, a probabilistic model which has performed well on information extraction tasks because of its ability to capture arbitrary, overlapping features of the input in a Markov model. We implement several techniques to estimate the confidence of both extracted fields and entire multi-field records, obtaining an average precision of 98% for retrieving correct fields and 87% for multi-field records.

Information extraction techniques

information extraction system

linear-chain conditional random field -LRB- CRF -RRB-

probabilistic model

Markov model

structured databases

unstructured data sources

Web

newswire documents

multi-field records

multi-field records

systems

system

input

techniques

extracted fields

accuracy

average precision

information extraction tasks

arbitrary, overlapping features

Information extraction techniques

structured databases

unstructured data sources

Information extraction techniques

linear-chain conditional random field -LRB- CRF -RRB-

information extraction system

probabilistic model

information extraction tasks

probabilistic model

arbitrary, overlapping features

Web

unstructured data sources

newswire documents

unstructured data sources

linear-chain conditional random field -LRB- CRF -RRB-

probabilistic model

Web

newswire documents

extracted fields

multi-field records

accuracy

systems

average precision

techniques

arbitrary, overlapping features

input

arbitrary, overlapping features

Markov model


Input: In this paper, we use the information redundancy in multilingual input to correct errors in machine translation and thus improve the quality of multilingual summaries. We consider the case of multi-document summarization, where the input documents are in Arabic, and the output summary is in English. Typically, information that makes it to a summary appears in many different lexical-syntactic forms in the input documents. Further, the use of multiple machine translation systems provides yet more redundancy, yielding different ways to realize that information in English. We demonstrate how errors in the machine translations of the input Arabic documents can be corrected by identifying and generating from such redundancy, focusing on noun phrases.

information redundancy in multilingual input

lexical-syntactic forms

machine translation

multilingual summaries

multi-document summarization

machine translations

Arabic

English

English

Arabic documents

machine translation systems

information redundancy in multilingual input

machine translation

information redundancy in multilingual input

multilingual summaries

Arabic documents

machine translations


Input: In this paper, we propose a new approach to generate oriented object proposals -LRB- OOPs -RRB- to reduce the detection error caused by various orientations of the object. To this end, we propose to efficiently locate object regions according to pixelwise object probability, rather than measuring the objectness from a set of sampled windows. We formulate the proposal generation problem as a generative proba-bilistic model such that object proposals of different shapes -LRB- i.e., sizes and orientations -RRB- can be produced by locating the local maximum likelihoods. The new approach has three main advantages. First, it helps the object detector handle objects of different orientations. Second, as the shapes of the proposals may vary to fit the objects, the resulting proposals are tighter than the sampling windows with fixed sizes. Third, it avoids massive window sampling, and thereby reducing the number of proposals while maintaining a high recall. Experiments on the PASCAL VOC 2007 dataset show that the proposed OOP outperforms the state-of-the-art fast methods. Further experiments show that the rotation invariant property helps a class-specific object detector achieve better performance than the state-of-the-art proposal generation methods in either object rotation scenarios or general scenarios. Generating OOPs is very fast and takes only 0.5 s per image.

approach

approach

it

it

state-of-the-art fast methods

oriented object proposals -LRB- OOPs -RRB-

proposal generation problem

OOPs

detection error

recall

orientations of the object

object regions

pixelwise object probability

objectness

object proposals

shapes

sizes

orientations

local maximum likelihoods

orientations

shapes of the proposals

sampling windows

number of proposals

rotation invariant property

generative proba-bilistic model

object detector

massive window sampling

OOP

class-specific object detector

proposal generation methods

PASCAL VOC 2007 dataset

object rotation scenarios

general scenarios

approach

oriented object proposals -LRB- OOPs -RRB-

pixelwise object probability

object regions

generative proba-bilistic model

proposal generation problem

local maximum likelihoods

object proposals

object detector

orientations

it

number of proposals

rotation invariant property

class-specific object detector

detection error

oriented object proposals -LRB- OOPs -RRB-

recall

it

PASCAL VOC 2007 dataset

OOP

object rotation scenarios

class-specific object detector

object rotation scenarios

proposal generation methods

general scenarios

class-specific object detector

general scenarios

proposal generation methods

pixelwise object probability

objectness

OOP

state-of-the-art fast methods

class-specific object detector

proposal generation methods

shapes

object proposals

sizes

shapes

orientations

shapes

sizes

orientations

object rotation scenarios

general scenarios


Input: This paper describes three relatively domain-independent capabilities recently added to the Paramax spoken language understanding system: non-monotonic reasoning, implicit reference resolution, and database query paraphrase. In addition, we discuss the results of the February 1992 ATIS benchmark tests. We describe a variation on the standard evaluation metric which provides a more tightly controlled measure of progress. Finally, we briefly describe an experiment which we have done in extending the n-best speech/language integration architecture to improving OCR accuracy.

domain-independent capabilities

Paramax spoken language understanding system

n-best speech/language integration architecture

non-monotonic reasoning

implicit reference resolution

database query paraphrase

February 1992 ATIS benchmark tests

OCR accuracy

domain-independent capabilities

Paramax spoken language understanding system

non-monotonic reasoning

domain-independent capabilities

implicit reference resolution

domain-independent capabilities

database query paraphrase

domain-independent capabilities

OCR accuracy

n-best speech/language integration architecture


Input: We investigate the problem of fine-grained sketch-based image retrieval -LRB- SBIR -RRB-, where free-hand human sketches are used as queries to perform instance-level retrieval of images. This is an extremely challenging task because -LRB- i -RRB- visual comparisons not only need to be fine-grained but also executed cross-domain, -LRB- ii -RRB- free-hand -LRB- finger -RRB- sketches are highly abstract, making fine-grained matching harder, and most importantly -LRB- iii -RRB- annotated cross-domain sketch-photo datasets required for training are scarce, challenging many state-of-the-art machine learning techniques. In this paper, for the first time, we address all these challenges, providing a step towards the capabilities that would underpin a commercial sketch-based image retrieval application. We introduce a new database of 1,432 sketch-photo pairs from two categories with 32,000 fine-grained triplet ranking annotations. We then develop a deep triplet-ranking model for instance-level SBIR with a novel data augmentation and staged pre-training strategy to alleviate the issue of insufficient fine-grained training data. Extensive experiments are carried out to contribute a variety of insights into the challenges of data sufficiency and over-fitting avoidance when training deep networks for fine-grained cross-domain ranking tasks.

fine-grained sketch-based image retrieval -LRB- SBIR -RRB-

instance-level retrieval of images

fine-grained matching

sketch-based image retrieval application

instance-level SBIR

fine-grained cross-domain ranking tasks

free-hand human sketches

visual comparisons

free-hand -LRB- finger -RRB- sketches

fine-grained triplet ranking annotations

data sufficiency

over-fitting avoidance

annotated cross-domain sketch-photo datasets

insufficient fine-grained training data

machine learning techniques

deep triplet-ranking model

data augmentation

staged pre-training strategy

deep networks

free-hand human sketches

instance-level retrieval of images

annotated cross-domain sketch-photo datasets

machine learning techniques

deep triplet-ranking model

instance-level SBIR

deep triplet-ranking model

insufficient fine-grained training data

data augmentation

deep triplet-ranking model

staged pre-training strategy

deep triplet-ranking model

deep networks

fine-grained cross-domain ranking tasks

data augmentation

staged pre-training strategy

data sufficiency

over-fitting avoidance


Input: In this paper we target at generating generic action proposals in unconstrained videos. Each action proposal corresponds to a temporal series of spatial bounding boxes, i.e., a spatio-temporal video tube, which has a good potential to locate one human action. Assuming each action is performed by a human with meaningful motion, both appearance and motion cues are utilized to measure the ac-tionness of the video tubes. After picking those spatiotem-poral paths of high actionness scores, our action proposal generation is formulated as a maximum set coverage problem, where greedy search is performed to select a set of action proposals that can maximize the overall actionness score. Compared with existing action proposal approaches, our action proposals do not rely on video segmentation and can be generated in nearly real-time. Experimental results on two challenging datasets, MSRII and UCF 101, validate the superior performance of our action proposals as well as competitive results on action detection and search.

generic action proposals

action proposal

temporal series of spatial bounding boxes

spatio-temporal video tube

human action

appearance and motion cues

action proposals

action proposals

action proposals

unconstrained videos

video tubes

MSRII

UCF 101

ac-tionness

actionness scores

actionness score

action proposal generation

maximum set coverage problem

action detection and search

greedy search

action proposal approaches

video segmentation

datasets

unconstrained videos

generic action proposals

spatio-temporal video tube

human action

appearance and motion cues

ac-tionness

maximum set coverage problem

action proposal generation

greedy search

action proposals

spatio-temporal video tube

temporal series of spatial bounding boxes

MSRII

datasets

UCF 101

datasets

ac-tionness

video tubes

actionness score

action proposals

datasets

action proposals

action detection and search

action proposals

action proposal approaches

action proposals

MSRII

UCF 101


Input: This paper reports recent research into methods for creating natural language text. A new processing paradigm called Fragment-and-Compose has been created and an experimental system implemented in it. The knowledge to be expressed in text is first divided into small propositional units, which are then composed into appropriate combinations and converted into text. KDS -LRB- Knowledge Delivery System -RRB-, which embodies this paradigm, has distinct parts devoted to creation of the propositional units, to organization of the text, to prevention of excess redundancy, to creation of combinations of units, to evaluation of these combinations as potential sentences, to selection of the best among competing combinations, and to creation of the final text. The Fragment-and-Compose paradigm and the computational methods of KDS are described.

methods

processing paradigm

paradigm

computational methods

creating natural language text

Fragment-and-Compose

KDS -LRB- Knowledge Delivery System -RRB-

Fragment-and-Compose paradigm

KDS

methods

creating natural language text

computational methods

KDS

paradigm

KDS -LRB- Knowledge Delivery System -RRB-


Input: This paper explores the issue of using different co-occurrence similarities between terms for separating query terms that are useful for retrieval from those that are harmful. The hypothesis under examination is that useful terms tend to be more similar to each other than to other query terms. Preliminary experiments with similarities computed using first-order and second-order co-occurrence seem to confirm the hypothesis. Term similarities could then be used for determining which query terms are useful and best reflect the user's information need. A possible application would be to use this source of evidence for tuning the weights of the query terms.

co-occurrence similarities

first-order and second-order co-occurrence

Term similarities

query terms

those

useful terms

query terms

similarities

retrieval

co-occurrence similarities

query terms

query terms

retrieval

first-order and second-order co-occurrence

similarities

those

query terms

useful terms

query terms


Input: We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models. Within our framework, we carry out a large number of experiments to understand better and explain why phrase-based models outperform word-based models. Our empirical results, which hold for all examined language pairs, suggest that the highest levels of performance can be obtained through relatively simple means: heuristic learning of phrase translations from word-based alignments and lexical weighting of phrase translations. Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy word-level alignment models does not have a strong impact on performance. Learning only syntactically motivated phrases degrades the performance of our systems.

phrase-based translation model

decoding algorithm

phrase-based translation models

phrase-based models

word-based models

heuristic learning of phrase translations

word-based alignments

lexical weighting of phrase translations

high-accuracy word-level alignment models

framework

means

systems

phrase-based translation model

decoding algorithm

phrase-based models

word-based models

heuristic learning of phrase translations

means

lexical weighting of phrase translations

means

word-based alignments

heuristic learning of phrase translations


Input: Color is known to be highly discriminative for many object recognition tasks, but is difficult to infer from uncontrolled images in which the illuminant is not known. Traditional methods for color constancy can improve surface re-flectance estimates from such uncalibrated images, but their output depends significantly on the background scene. In many recognition and retrieval applications, we have access to image sets that contain multiple views of the same object in different environments; we show in this paper that correspondences between these images provide important constraints that can improve color constancy. We introduce the multi-view color constancy problem, and present a method to recover estimates of underlying surface re-flectance based on joint estimation of these surface properties and the illuminants present in multiple images. The method can exploit image correspondences obtained by various alignment techniques, and we show examples based on matching local region features. Our results show that multi-view constraints can significantly improve estimates of both scene illuminants and object color -LRB- surface reflectance -RRB- when compared to a baseline single-view method.

object recognition tasks

color constancy

recognition and retrieval applications

color constancy

multi-view color constancy problem

estimates of underlying surface re-flectance

estimates of both scene illuminants and object color -LRB- surface reflectance -RRB-

uncontrolled images

uncalibrated images

illuminant

background scene

surface properties

illuminants

image correspondences

matching local region features

multi-view constraints

methods

method

method

surface re-flectance estimates

alignment techniques

baseline single-view method

methods

color constancy

methods

surface re-flectance estimates

uncalibrated images

surface re-flectance estimates

method

estimates of underlying surface re-flectance

method

image correspondences

alignment techniques

image correspondences

multi-view constraints

estimates of both scene illuminants and object color -LRB- surface reflectance -RRB-

baseline single-view method

multi-view constraints


Input: We describe a dialogue system that works with its interlocutor to identify objects. Our contributions include a concise, modular architecture with reversible processes of understanding and generation, an information-state model of reference, and flexible links between semantics and collaborative problem solving.

dialogue system

concise, modular architecture

information-state model of reference

understanding

generation

collaborative problem solving

semantics

concise, modular architecture

understanding

concise, modular architecture

generation

understanding

generation