Input: The theoretical study of the range concatenation grammar -LSB- RCG -RSB- formalism has revealed many attractive properties which may be used in NLP. In particular, range concatenation languages -LSB- RCL -RSB- can be parsed in polynomial time and many classical grammatical formalisms can be translated into equivalent RCGs without increasing their worst-case parsing time complexity. For example, after translation into an equivalent RCG, any tree adjoining grammar can be parsed in O -LRB- n6 -RRB- time. In this paper, we study a parsing technique whose purpose is to improve the practical efficiency of RCL parsers. The non-deterministic parsing choices of the main parser for a language L are directed by a guide which uses the shared derivation forest output by a prior RCL parser for a suitable superset of L. The results of a practical evaluation of this method on a wide coverage English grammar are given.

range concatenation grammar -LSB- RCG -RSB- formalism

grammatical formalisms

RCGs

RCG

tree adjoining grammar

parsing technique

RCL parsers

non-deterministic parsing

main parser

RCL parser

wide coverage English grammar

NLP

range concatenation languages -LSB- RCL -RSB-

polynomial time

O -LRB- n6 -RRB- time

language L

shared derivation forest

worst-case parsing time complexity

method

range concatenation grammar -LSB- RCG -RSB- formalism

NLP

parsing technique

RCL parsers

main parser

language L

RCL parser

shared derivation forest

polynomial time

range concatenation languages -LSB- RCL -RSB-

O -LRB- n6 -RRB- time

tree adjoining grammar

worst-case parsing time complexity

grammatical formalisms

wide coverage English grammar

method


Input: In this paper we introduce Ant-Q, a family of algorithms which present many similarities with Q-learning -LRB- Watkins, 1989 -RRB-, and which we apply to the solution of symmetric and asym-metric instances of the traveling salesman problem -LRB- TSP -RRB-. Ant-Q algorithms were inspired by work on the ant system -LRB- AS -RRB-, a distributed algorithm for combinatorial optimization based on the metaphor of ant colonies which was recently proposed in -LRB- Dorigo, 1992; Dorigo, Maniezzo and Colorni, 1996 -RRB-. We show that AS is a particular instance of the Ant-Q family, and that there are instances of this family which perform better than AS. We experimentally investigate the functioning of Ant-Q and we show that the results obtained by Ant-Q on symmetric TSP's are competitive with those obtained by other heuristic approaches based on neural networks or local search. Finally, we apply Ant-Q to some difficult asymmetric TSP's obtaining very good results: Ant-Q was able to find solutions of a quality which usually can be found only by very specialized algorithms.

Ant-Q

Q-learning

Ant-Q algorithms

ant system -LRB- AS -RRB-

distributed algorithm

AS

Ant-Q family

Ant-Q

Ant-Q

heuristic approaches

neural networks

local search

Ant-Q

Ant-Q

symmetric and asym-metric instances of the traveling salesman problem -LRB- TSP -RRB-

combinatorial optimization

symmetric TSP

asymmetric TSP

instances

family

AS

Ant-Q

symmetric and asym-metric instances of the traveling salesman problem -LRB- TSP -RRB-

ant system -LRB- AS -RRB-

Ant-Q algorithms

distributed algorithm

combinatorial optimization

Ant-Q

symmetric TSP

neural networks

heuristic approaches

local search

heuristic approaches

Ant-Q

asymmetric TSP

ant system -LRB- AS -RRB-

distributed algorithm

AS

Ant-Q family

instances

family

instances

AS

Ant-Q

heuristic approaches

neural networks

local search


Input: In this paper, we develop a geometric framework for linear or nonlinear discriminant subspace learning and classification. In our framework, the structures of classes are conceptualized as a semi-Riemannian manifold which is considered as a submanifold embedded in an ambient semi-Riemannian space. The class structures of original samples can be characterized and deformed by local metrics of the semi-Riemannian space. Semi-Riemannian metrics are uniquely determined by the smoothing of discrete functions and the nullity of the semi-Riemannian space. Based on the geometrization of class structures, optimizing class structures in the feature space is equivalent to maximizing the quadratic quantities of metric tensors in the semi-Riemannian space. Thus supervised discriminant subspace learning reduces to unsupervised semi-Riemannian mani-fold learning. Based on the proposed framework, a novel algorithm, dubbed as Semi-Riemannian Discriminant Analysis -LRB- SRDA -RRB-, is presented for subspace-based classification. The performance of SRDA is tested on face recognition -LRB- singular case -RRB- and handwritten capital letter classification -LRB- nonsingular case -RRB- against existing algorithms. The experimental results show that SRDA works well on recognition and classification, implying that semi-Riemannian geometry is a promising new tool for pattern recognition and machine learning.

geometric framework

smoothing of discrete functions

geometrization of class structures

supervised discriminant subspace learning

unsupervised semi-Riemannian mani-fold learning

Semi-Riemannian Discriminant Analysis -LRB- SRDA -RRB-

SRDA

semi-Riemannian geometry

linear or nonlinear discriminant subspace learning and classification

subspace-based classification

face recognition -LRB- singular case

handwritten capital letter classification -LRB- nonsingular case -RRB-

recognition

classification

pattern recognition

machine learning

framework

framework

algorithm

algorithms

SRDA

structures of classes

semi-Riemannian manifold

submanifold

ambient semi-Riemannian space

class structures

nullity of the semi-Riemannian space

class structures

feature space

quadratic quantities of metric tensors

semi-Riemannian space

local metrics of the semi-Riemannian space

Semi-Riemannian metrics

geometric framework

linear or nonlinear discriminant subspace learning and classification

semi-Riemannian manifold

structures of classes

local metrics of the semi-Riemannian space

class structures

smoothing of discrete functions

Semi-Riemannian metrics

nullity of the semi-Riemannian space

Semi-Riemannian metrics

framework

algorithm

algorithm

subspace-based classification

SRDA

recognition

SRDA

classification

semi-Riemannian geometry

pattern recognition

semi-Riemannian geometry

machine learning

submanifold

ambient semi-Riemannian space

smoothing of discrete functions

nullity of the semi-Riemannian space

face recognition -LRB- singular case

handwritten capital letter classification -LRB- nonsingular case -RRB-

recognition

classification

pattern recognition

machine learning

feature space

class structures

semi-Riemannian space

quadratic quantities of metric tensors

SRDA

algorithms

face recognition -LRB- singular case

SRDA

face recognition -LRB- singular case

algorithms

handwritten capital letter classification -LRB- nonsingular case -RRB-

SRDA

handwritten capital letter classification -LRB- nonsingular case -RRB-

algorithms


Input: A deterministic parser is under development which represents a departure from traditional deterministic parsers in that it combines both symbolic and connectionist components. The connectionist component is trained either from patterns derived from the rules of a deterministic grammar. The development and evolution of such a hybrid architecture has lead to a parser which is superior to any known deterministic parser. Experiments are described and powerful training techniques are demonstrated that permit decision-making by the connectionist component in the parsing process. This approach has permitted some simplifications to the rules of other deterministic parsers, including the elimination of rule packets and priorities. Furthermore, parsing is performed more robustly and with more tolerance for error. Data are presented which show how a connectionist -LRB- neural -RRB- network trained with linguistic rules can parse both expected -LRB- grammatical -RRB- sentences as well as some novel -LRB- ungrammatical or lexically ambiguous -RRB- sentences.

deterministic parser

deterministic parsers

symbolic and connectionist components

connectionist component

rules of a deterministic grammar

hybrid architecture

parser

deterministic parser

training techniques

connectionist component

parsing process

deterministic parsers

connectionist -LRB- neural -RRB- network

it

approach

patterns

rules

linguistic rules

decision-making

parsing

expected -LRB- grammatical -RRB- sentences

-LRB- ungrammatical or lexically ambiguous -RRB- sentences

deterministic parser

deterministic parsers

parser

deterministic parser

symbolic and connectionist components

it

connectionist component

parsing process

patterns

connectionist component

rules of a deterministic grammar

patterns

hybrid architecture

parser

training techniques

decision-making

connectionist component

decision-making

connectionist -LRB- neural -RRB- network

expected -LRB- grammatical -RRB- sentences

connectionist -LRB- neural -RRB- network

-LRB- ungrammatical or lexically ambiguous -RRB- sentences

linguistic rules

connectionist -LRB- neural -RRB- network

expected -LRB- grammatical -RRB- sentences

-LRB- ungrammatical or lexically ambiguous -RRB- sentences


Input: Robust natural language interpretation requires strong semantic domain models, fail-soft recovery heuristics, and very flexible control structures. Although single-strategy parsers have met with a measure of success, a multi-strategy approach is shown to provide a much higher degree of flexibility, redundancy, and ability to bring task-specific domain knowledge -LRB- in addition to general linguistic knowledge -RRB- to bear on both grammatical and ungrammatical input. A parsing algorithm is presented that integrates several different parsing strategies, with case-frame instantiation dominating. Each of these parsing strategies exploits different types of knowledge; and their combination provides a strong framework in which to process conjunctions, fragmentary input, and ungrammatical structures, as well as less exotic, grammatically correct input. Several specific heuristics for handling ungrammatical input are presented within this multi-strategy framework.

natural language interpretation

semantic domain models

fail-soft recovery heuristics

single-strategy parsers

multi-strategy approach

parsing algorithm

parsing strategies

parsing strategies

specific heuristics

multi-strategy framework

control structures

task-specific domain knowledge

general linguistic knowledge

grammatical and ungrammatical input

case-frame instantiation

conjunctions

fragmentary input

ungrammatical structures

exotic, grammatically correct input

ungrammatical input

semantic domain models

natural language interpretation

fail-soft recovery heuristics

natural language interpretation

control structures

natural language interpretation

parsing strategies

conjunctions

parsing strategies

fragmentary input

parsing strategies

ungrammatical structures

parsing strategies

exotic, grammatically correct input

specific heuristics

ungrammatical input

semantic domain models

fail-soft recovery heuristics

fail-soft recovery heuristics

control structures

task-specific domain knowledge

general linguistic knowledge

conjunctions

fragmentary input

fragmentary input

ungrammatical structures

ungrammatical structures

exotic, grammatically correct input

single-strategy parsers

multi-strategy approach

parsing strategies

parsing algorithm

specific heuristics

multi-strategy framework

case-frame instantiation

parsing strategies


Input: Recently, Stacked Auto-Encoders -LRB- SAE -RRB- have been successfully used for learning imbalanced datasets. In this paper, for the first time, we propose to use a Neural Network classifier furnished by an SAE structure for detecting the errors made by a strong Automatic Speech Recognition -LRB- ASR -RRB- system. Error detection on an automatic transcription provided by a'' strong'' ASR system, i.e. exhibiting a small word error rate, is difficult due to the limited number of'' positive'' examples -LRB- i.e. words erroneously recognized -RRB- available for training a binary classi-fier. In this paper we investigate and compare different types of classifiers for automatically detecting ASR errors, including the one based on a stacked auto-encoder architecture. We show the effectiveness of the latter by measuring and comparing performance on the automatic transcriptions of an English corpus collected from TED talks. Performance of each investigated classifier is evaluated both via receiving operating curve and via a measure, called mean absolute error, related to the quality in predicting the corresponding word error rate. The results demonstrates that the classifier based on SAE detects the ASR errors better than the other classification methods.

Stacked Auto-Encoders -LRB- SAE -RRB-

Neural Network classifier

SAE structure

Automatic Speech Recognition -LRB- ASR -RRB- system

ASR system

binary classi-fier

classifiers

stacked auto-encoder architecture

classifier

classifier

SAE

classification methods

learning imbalanced datasets

Error detection

automatically detecting ASR errors

automatic transcription

automatic transcriptions

English corpus

TED talks

word error rate

receiving operating curve

mean absolute error

word error rate

one

measure

ASR errors

Stacked Auto-Encoders -LRB- SAE -RRB-

learning imbalanced datasets

Neural Network classifier

Automatic Speech Recognition -LRB- ASR -RRB- system

SAE structure

Neural Network classifier

Error detection

automatic transcription

classifiers

automatically detecting ASR errors

stacked auto-encoder architecture

one

TED talks

English corpus

classifier

ASR errors

SAE

classifier

classification methods

ASR errors

one

classifiers

English corpus

automatic transcriptions

receiving operating curve

classifier

measure

classifier

receiving operating curve

measure

classifier

classification methods


Input: Within the EU Network of Excellence PASCAL, a challenge was organized to design a statistical machine learning algorithm that segments words into the smallest meaning-bearing units of language, morphemes. Ideally, these are basic vocabulary units suitable for different tasks, such as speech and text understanding, machine translation, information retrieval, and statistical language modeling. Twelve research groups participated in the challenge and had submitted segmentation results obtained by their algorithms. In this paper, we evaluate the application of these segmen-tation algorithms to large vocabulary speech recognition using statistical n-gram language models based on the proposed word segments instead of entire words. Experiments were done for two ag-glutinative and morphologically rich languages: Finnish and Turk-ish. We also investigate combining various segmentations to improve the performance of the recognizer.

statistical machine learning algorithm

segmen-tation algorithms

statistical n-gram language models

smallest meaning-bearing units of language

morphemes

these

tasks

speech and text understanding

machine translation

information retrieval

statistical language modeling

large vocabulary speech recognition

ag-glutinative and morphologically rich languages

Finnish

Turk-ish

statistical machine learning algorithm

smallest meaning-bearing units of language

these

tasks

segmen-tation algorithms

large vocabulary speech recognition

morphemes

smallest meaning-bearing units of language

speech and text understanding

tasks

machine translation

tasks

information retrieval

tasks

statistical language modeling

tasks

Finnish

ag-glutinative and morphologically rich languages

Turk-ish

ag-glutinative and morphologically rich languages

speech and text understanding

machine translation

machine translation

information retrieval

information retrieval

statistical language modeling

Finnish

Turk-ish

statistical n-gram language models

segmen-tation algorithms


Input: This paper describes a recently collected spoken language corpus for the ATIS -LRB- Air Travel Information System -RRB- domain. This data collection effort has been co-ordinated by MADCOW -LRB- Multi-site ATIS Data COllection Working group -RRB-. We summarize the motivation for this effort, the goals, the implementation of a multi-site data collection paradigm, and the accomplishments of MADCOW in monitoring the collection and distribution of 12,000 utterances of spontaneous speech from five sites for use in a multi-site common evaluation of speech, natural language and spoken language.

spoken language corpus

spontaneous speech

ATIS -LRB- Air Travel Information System -RRB- domain

multi-site common evaluation of speech, natural language and spoken language

data collection

collection

multi-site data collection paradigm

spoken language corpus

ATIS -LRB- Air Travel Information System -RRB- domain

spontaneous speech

multi-site common evaluation of speech, natural language and spoken language


Input: This paper proposes the Hierarchical Directed Acyclic Graph -LRB- HDAG -RRB- Kernel for structured natural language data. The HDAG Kernel directly accepts several levels of both chunks and their relations, and then efficiently computes the weighed sum of the number of common attribute sequences of the HDAGs. We applied the proposed method to question classification and sentence alignment tasks to evaluate its performance as a similarity measure and a kernel function. The results of the experiments demonstrate that the HDAG Kernel is superior to other kernel functions and baseline methods.

Hierarchical Directed Acyclic Graph -LRB- HDAG -RRB- Kernel

HDAG Kernel

HDAGs

kernel function

HDAG Kernel

kernel functions

structured natural language data

method

baseline methods

question classification and sentence alignment tasks

similarity measure

Hierarchical Directed Acyclic Graph -LRB- HDAG -RRB- Kernel

structured natural language data

method

question classification and sentence alignment tasks

similarity measure

method

kernel function

method

similarity measure

kernel function

kernel functions

baseline methods

HDAG Kernel

kernel functions

HDAG Kernel

baseline methods


Input: We propose a solution to the challenge of the CoNLL 2008 shared task that uses a generative history-based latent variable model to predict the most likely derivation of a synchronous dependency parser for both syntactic and semantic dependencies. The submitted model yields 79.1% macro-average F1 performance, for the joint task, 86.9% syntactic dependencies LAS and 71.0% semantic dependencies F1. A larger model trained after the deadline achieves 80.5% macro-average F1, 87.6% syntactic dependencies LAS, and 73.1% semantic dependencies F1.

CoNLL 2008 shared task

synchronous dependency parser

generative history-based latent variable model

syntactic and semantic dependencies

model

task

model

macro-average F1 performance

syntactic dependencies LAS

semantic dependencies F1

macro-average F1

syntactic dependencies LAS

semantic dependencies F1

generative history-based latent variable model

CoNLL 2008 shared task

generative history-based latent variable model

synchronous dependency parser

synchronous dependency parser

syntactic and semantic dependencies

macro-average F1 performance

model

macro-average F1 performance

task

syntactic dependencies LAS

task

semantic dependencies F1

task

macro-average F1

model

syntactic dependencies LAS

model

semantic dependencies F1

model

syntactic dependencies LAS

semantic dependencies F1

macro-average F1

syntactic dependencies LAS

syntactic dependencies LAS

semantic dependencies F1


Input: This paper describes a new, large scale discourse-level annotation project-the Penn Discourse TreeBank -LRB- PDTB -RRB-. We present an approach to annotating a level of discourse structure that is based on identifying discourse connectives and their arguments. The PDTB is being built directly on top of the Penn TreeBank and Propbank, thus supporting the extraction of useful syntactic and semantic features and providing a richer substrate for the development and evaluation of practical algorithms. We provide a detailed preliminary analysis of inter-annotator agreement-both the level of agreement and the types of inter-annotator variation.

large scale discourse-level annotation

extraction of useful syntactic and semantic features

Penn Discourse TreeBank -LRB- PDTB -RRB-

PDTB

Penn TreeBank

Propbank

approach

discourse structure

discourse connectives

inter-annotator variation

practical algorithms

inter-annotator agreement

level of agreement

approach

discourse structure

discourse connectives

approach

PDTB

extraction of useful syntactic and semantic features

Penn TreeBank

PDTB

Propbank

PDTB

PDTB

practical algorithms

Penn TreeBank

Propbank

level of agreement

inter-annotator variation

level of agreement

inter-annotator agreement

inter-annotator variation

inter-annotator agreement


Input: Currently, N-gram models are the most common and widely used models for statistical language modeling. In this paper, we investigated an alternative way to build language models, i.e., using artificial neural networks to learn the language model. Our experiment result shows that the neural network can learn a language model that has performance even better than standard statistical methods.

N-gram models

language models

artificial neural networks

language model

neural network

language model

statistical methods

statistical language modeling

N-gram models

statistical language modeling

artificial neural networks

language model

neural network

language model

neural network

statistical methods


Input: Automated facial expression recognition has received increased attention over the past two decades. Existing works in the field usually do not encode either the temporal evolution or the intensity of the observed facial displays. They also fail to jointly model multidimensional -LRB- multi-class -RRB- continuous facial behaviour data; binary classifiers-one for each target basic-emotion class-are used instead. In this paper, intrinsic topology of multidimensional continuous facial affect data is first modeled by an ordinal man-ifold. This topology is then incorporated into the Hidden Conditional Ordinal Random Field -LRB- H-CORF -RRB- framework for dynamic ordinal regression by constraining H-CORF parameters to lie on the ordinal manifold. The resulting model attains simultaneous dynamic recognition and intensity estimation of facial expressions of multiple emotions. To the best of our knowledge, the proposed method is the first one to achieve this on both deliberate as well as spontaneous facial affect data.

Automated facial expression recognition

dynamic ordinal regression

simultaneous dynamic recognition

intensity estimation of facial expressions

Existing works

They

topology

model

temporal evolution

intensity of the observed facial displays

intrinsic topology of multidimensional continuous facial

ordinal man-ifold

H-CORF parameters

ordinal manifold

multidimensional -LRB- multi-class -RRB- continuous facial behaviour data

spontaneous facial affect data

Hidden Conditional Ordinal Random Field -LRB- H-CORF -RRB- framework

the proposed method

intensity of the observed facial displays

temporal evolution

simultaneous dynamic recognition

intensity estimation of facial expressions

ordinal man-ifold

intrinsic topology of multidimensional continuous facial

Hidden Conditional Ordinal Random Field -LRB- H-CORF -RRB- framework

dynamic ordinal regression

model

simultaneous dynamic recognition

model

intensity estimation of facial expressions

topology

Hidden Conditional Ordinal Random Field -LRB- H-CORF -RRB- framework

spontaneous facial affect data

the proposed method


Input: Recent advances in linear classification have shown that for applications such as document classification, the training can be extremely efficient. However, most of the existing training methods are designed by assuming that data can be stored in the computer memory. These methods can not be easily applied to data larger than the memory capacity due to the random access to the disk. We propose and analyze a block minimization framework for data larger than the memory size. At each step a block of data is loaded from the disk and handled by certain learning methods. We investigate two implementations of the proposed framework for primal and dual SVMs, respectively. As data can not fit in memory, many design considerations are very different from those for traditional algorithms. Experiments using data sets 20 times larger than the memory demonstrate the effectiveness of the proposed method.

linear classification

document classification

applications

methods

data

data

framework

method

training methods

block minimization framework

learning methods

primal and dual SVMs

computer memory

memory capacity

random access

disk

memory size

disk

document classification

applications

data

memory capacity

data

memory size

block minimization framework

data

framework

primal and dual SVMs


Input: Instances of a word drawn from different domains may have different sense priors -LRB- the proportions of the different senses of a word -RRB-. This in turn affects the accuracy of word sense disambiguation -LRB- WSD -RRB- systems trained and applied on different domains. This paper presents a method to estimate the sense priors of words drawn from a new domain, and highlights the importance of using well calibrated probabilities when performing these estimations. By using well calibrated probabilities, we are able to estimate the sense priors effectively to achieve significant improvements in WSD accuracy.

sense priors

sense priors of words

well calibrated probabilities

well calibrated probabilities

sense priors

accuracy

WSD accuracy

word sense disambiguation -LRB- WSD -RRB- systems

method

estimations

new domain

accuracy

word sense disambiguation -LRB- WSD -RRB- systems

method

sense priors of words

well calibrated probabilities

estimations

well calibrated probabilities

sense priors

new domain

sense priors of words


Input: In this paper we deal with a recently developed large Czech MWE database containing at the moment 160 000 MWEs -LRB- treated as lexical units -RRB-. It was compiled from various resources such as encyclopedias and dictionaries, public databases of proper names and toponyms, collocations obtained from Czech WordNet, lists of botanical and zoological terms and others. We describe the structure of the database and give basic types of MWEs according to domains they belong to. We compare the built MWEs database with the corpus data from Czech National Corpus -LRB- approx. 100 mil. tokens -RRB- and present results of this comparison in the paper. These MWEs have not been obtained from the corpus since their frequencies in it are rather low. To obtain a more complete list of MWEs we propose and use a technique exploiting the Word Sketch Engine, which allows us to work with statistical parameters such as frequency of MWEs and their components as well as with the salience for the whole MWEs. We also discuss exploitation of the database for working out a more adequate tagging and lemmatization. The final goal is to be able to recognize MWEs in corpus text and lemmatize them as complete lexical units, i. e. to make tagging and lemmatization more adequate.

large Czech MWE database

encyclopedias

dictionaries

public databases of proper names and toponyms

Czech WordNet

lists of botanical and zoological terms

MWEs database

Czech National Corpus

MWEs

lexical units

MWEs

MWEs

MWEs

statistical parameters

MWEs

MWEs

MWEs

lexical units

It

collocations

database

corpus

technique

database

them

Word Sketch Engine

tagging

lemmatization

tagging

lemmatization

encyclopedias

It

dictionaries

It

public databases of proper names and toponyms

It

collocations

It

lists of botanical and zoological terms

It

Czech National Corpus

MWEs database

technique

Word Sketch Engine

database

tagging

database

lemmatization

MWEs

tagging

MWEs

lemmatization

encyclopedias

dictionaries

lists of botanical and zoological terms

collocations

tagging

lemmatization

tagging

lemmatization

statistical parameters

Word Sketch Engine


Input: We describe the ongoing construction of a large, semantically annotated corpus resource as reliable basis for the large-scale acquisition of word-semantic information, e.g. the construction of domain-independent lexica. The backbone of the annotation are semantic roles in the frame semantics paradigm. We report experiences and evaluate the annotated data from the first project stage. On this basis, we discuss the problems of vagueness and ambiguity in semantic annotation.

semantically annotated corpus

annotated data

large-scale acquisition of word-semantic information

construction of domain-independent lexica

annotation

semantic roles

vagueness

ambiguity

frame semantics paradigm

semantic annotation

semantically annotated corpus

large-scale acquisition of word-semantic information

construction of domain-independent lexica

large-scale acquisition of word-semantic information

semantic roles

frame semantics paradigm

vagueness

ambiguity

vagueness

semantic annotation

ambiguity

semantic annotation


Input: Statistical machine translation -LRB- SMT -RRB- is currently one of the hot spots in natural language processing. Over the last few years dramatic improvements have been made, and a number of comparative evaluations have shown, that SMT gives competitive results to rule-based translation systems, requiring significantly less development time. This is particularly important when building translation systems for new language pairs or new domains. This workshop is intended to give an introduction to statistical machine translation with a focus on practical considerations. Participants should be able, after attending this workshop, to set out building an SMT system themselves and achieving good baseline results in a short time. The tutorial will cover the basics of SMT: Theory will be put into practice. STTK, a statistical machine translation tool kit, will be introduced and used to build a working translation system. STTK has been developed by the presenter and co-workers over a number of years and is currently used as the basis of CMU's SMT system. It has also successfully been coupled with rule-based and example based machine translation modules to build a multi engine machine translation system. The source code of the tool kit will be made available.

Statistical machine translation -LRB- SMT -RRB-

SMT

rule-based translation systems

statistical machine translation

SMT system

SMT

STTK

statistical machine translation tool kit

STTK

SMT system

rule-based and example based machine translation modules

natural language processing

translation systems

translation system

multi engine machine translation system

new language pairs

new domains

It

tool kit

Statistical machine translation -LRB- SMT -RRB-

natural language processing

STTK

statistical machine translation tool kit

SMT

rule-based translation systems

translation systems

new language pairs

translation systems

new domains

STTK

translation system

STTK

SMT system

It

multi engine machine translation system

rule-based and example based machine translation modules

multi engine machine translation system

new language pairs

new domains

It

rule-based and example based machine translation modules


Input: This paper presents an unsupervised learning approach to building a non-English -LRB- Arabic -RRB- stemmer. The stemming model is based on statistical machine translation and it uses an English stemmer and a small -LRB- 10K sentences -RRB- parallel corpus as its sole training resources. No parallel text is needed after the training phase. Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre. Examples and results will be given for Arabic, but the approach is applicable to any language that needs affix removal. Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component. Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text, and 96% of the performance of the proprietary stemmer above.

unsupervised learning approach

stemming model

statistical machine translation

English stemmer

stemmer

resource-frugal approach

Arabic stemmer

unsupervised component

Task-based evaluation

stemmer

non-English -LRB- Arabic -RRB- stemmer

Arabic information retrieval

it

it

approach

parallel corpus

parallel text

Monolingual, unannotated text

Arabic

affix lists

human annotated text

unstemmed text

affix removal

rules

agreement

average precision

unsupervised learning approach

non-English -LRB- Arabic -RRB- stemmer

statistical machine translation

stemming model

English stemmer

it

parallel corpus

it

Monolingual, unannotated text

stemmer

rules

Arabic stemmer

affix lists

Arabic stemmer

human annotated text

Arabic stemmer

unsupervised component

Arabic stemmer

Arabic information retrieval

Task-based evaluation

resource-frugal approach

Arabic stemmer

agreement

resource-frugal approach

agreement

Arabic stemmer

average precision

Task-based evaluation

average precision

unstemmed text

rules

affix lists

affix lists

human annotated text

human annotated text

unsupervised component


Input: The paper assesses the capability of an HMM-based TTS system to produce German speech. The results are discussed in qualitative terms, and compared over three different choices of context features. In addition, the system is adapted to a small set of football announcements, in an exploratory attempt to synthe-sise expressive speech. We conclude that the HMMs are able to produce highly intelligible neutral German speech, with a stable quality, and that the expressivity is partially captured in spite of the small size of the football dataset.

HMM-based TTS system

HMMs

German speech

football announcements

expressive speech

intelligible neutral German speech

football dataset

context features

system

HMM-based TTS system

German speech

system

football announcements

system

expressive speech

HMMs

intelligible neutral German speech


Input: We give an analysis of ellipsis resolution in terms of a straightforward discourse copying algorithm that correctly predicts a wide range of phenomena. The treatment does not suffer from problems inherent in identity-of-relations analyses. Furthermore, in contrast to the approach of Dalrymple et al. -LSB- 1991 -RSB-, the treatment directly encodes the intuitive distinction between full NPs and the referential elements that corefer with them through what we term role linking. The correct predictions for several problematic examples of ellipsis naturally result. Finally, the analysis extends directly to other discourse copying phenomena.

analysis of ellipsis resolution

identity-of-relations analyses

role linking

discourse copying phenomena

discourse copying algorithm

treatment

treatment

analysis

full NPs

referential elements

ellipsis

full NPs

referential elements

analysis

discourse copying phenomena


Input: How to obtain hierarchical relations -LRB- e.g. superordinate-hyponym relation, synonym relation -RRB- is one of the most important problems for thesaurus construction. A pilot system for extracting these relations automatically from an ordinary Japanese language dictionary -LRB- Shinmeikai Kokugojiten, published by Sansei-do, in machine readable form -RRB- is given. The features of the definition sentences in the dictionary, the mechanical extraction of the hierarchical relations and the estimation of the results are discussed.

hierarchical relations

superordinate-hyponym relation

synonym relation

features

definition sentences

hierarchical relations

thesaurus construction

relations

dictionary

Japanese language dictionary

hierarchical relations

thesaurus construction

definition sentences

dictionary

superordinate-hyponym relation

hierarchical relations

synonym relation

hierarchical relations

superordinate-hyponym relation

synonym relation

Japanese language dictionary

relations

definition sentences

features


Input: Face images of non-frontal views under poor illumination with low resolution reduce dramatically face recognition accuracy. This is evident most compellingly by the very low recognition rate of all existing face recognition systems when applied to live CCTV camera input. In this paper, we present a Bayesian framework to perform multi-modal -LRB- such as variations in viewpoint and illumination -RRB- face image super-resolution for recognition in tensor space. Given a single modal low-resolution face image, we benefit from the multiple factor interactions of training tensor, and super-resolve its high-resolution reconstructions across different modalities for face recognition. Instead of performing pixel-domain super-resolution and recognition independently as two separate sequential processes, we integrate the tasks of super-resolution and recognition by directly computing a maximum likelihood identity parameter vector in high-resolution tensor space for recognition. We show results from multi-modal super-resolution and face recognition experiments across different imaging modalities, using low-resolution images as testing inputs and demonstrate improved recognition rates over standard tensorface and eigenface representations.

Face images of non-frontal views

face image super-resolution

recognition

high-resolution reconstructions

face recognition

pixel-domain super-resolution and recognition

super-resolution

recognition

recognition

multi-modal super-resolution and face recognition

face recognition accuracy

recognition rate

recognition rates

face recognition systems

Bayesian framework

tensorface and eigenface representations

live CCTV camera input

single modal low-resolution face image

low-resolution images

viewpoint

illumination

tensor space

multiple factor interactions of training tensor

modalities

maximum likelihood identity parameter vector

high-resolution tensor space

imaging modalities

recognition rate

face recognition systems

recognition rates

multi-modal super-resolution and face recognition

recognition rates

tensorface and eigenface representations

live CCTV camera input

face recognition systems

Bayesian framework

face image super-resolution

face image super-resolution

recognition

single modal low-resolution face image

high-resolution reconstructions

multiple factor interactions of training tensor

high-resolution reconstructions

high-resolution reconstructions

face recognition

maximum likelihood identity parameter vector

super-resolution

maximum likelihood identity parameter vector

recognition

maximum likelihood identity parameter vector

recognition

low-resolution images

multi-modal super-resolution and face recognition

viewpoint

illumination

super-resolution

recognition

tensor space

recognition

modalities

high-resolution reconstructions

high-resolution tensor space

maximum likelihood identity parameter vector

super-resolution

pixel-domain super-resolution and recognition

recognition

pixel-domain super-resolution and recognition


Input: In this paper, we describe a phrase-based unigram model for statistical machine translation that uses a much simpler set of model parameters than similar phrase-based models. The units of translation are blocks-pairs of phrases. During decoding, we use a block unigram model and a word-based trigram language model. During training, the blocks are learned from source interval projections using an underlying word alignment. We show experimental results on block selection criteria based on unigram counts and phrase length.

phrase-based unigram model

phrase-based models

block unigram model

word-based trigram language model

source interval projections

word alignment

statistical machine translation

decoding

training

model parameters

blocks

blocks

unigram counts

phrase length

block selection criteria

phrase-based unigram model

statistical machine translation

model parameters

phrase-based unigram model

block unigram model

decoding

word-based trigram language model

decoding

source interval projections

blocks

word alignment

source interval projections

unigram counts

block selection criteria

phrase length

block selection criteria

phrase-based unigram model

phrase-based models

word-based trigram language model

block unigram model

unigram counts

phrase length


Input: This paper develops a new approach for extremely fast detection in domains where the distribution of positive and negative examples is highly skewed -LRB- e.g. face detection or database retrieval -RRB-. In such domains a cascade of simple classifiers each trained to achieve high detection rates and modest false positive rates can yield a final detector with many desirable features: including high detection rates, very low false positive rates, and fast performance. Achieving extremely high detection rates, rather than low error, is not a task typically addressed by machine learning algorithms. We propose a new variant of AdaBoost as a mechanism for training the simple classifiers used in the cascade. Experimental results in the domain of face detection show the training algorithm yields significant improvements in performance over conventional AdaBoost. The final face detection system can process 15 frames per second, achieves over 90% detection, and a false positive rate of 1 in a 1,000,000.

approach

detector

cascade

fast detection

face detection

database retrieval

face detection

distribution

features

classifiers

positive and negative examples

cascade of simple classifiers

classifiers

machine learning algorithms

AdaBoost

training algorithm

AdaBoost

face detection system

detection rates

modest false positive rates

detection rates

false positive rates

fast performance

detection rates

low error

detection

false positive rate

approach

fast detection

cascade of simple classifiers

detector

AdaBoost

classifiers

classifiers

cascade

training algorithm

face detection

AdaBoost

face detection

face detection

database retrieval

detection rates

modest false positive rates

detection

false positive rate

detection rates

classifiers

modest false positive rates

classifiers

features

detector

detection rates

low error

AdaBoost

training algorithm


Input: This paper proposes a method for learning joint embed-dings of images and text using a two-branch neural network with multiple layers of linear projections followed by nonlinearities. The network is trained using a large-margin objective that combines cross-view ranking constraints with within-view neighborhood structure preservation constraints inspired by metric learning literature. Extensive experiments show that our approach gains significant improvements in accuracy for image-to-text and text-to-image retrieval. Our method achieves new state-of-the-art results on the Flickr30K and MSCOCO image-sentence datasets and shows promise on the new task of phrase lo-calization on the Flickr30K Entities dataset.

method

network

approach

method

joint embed-dings of images and text

multiple layers of linear projections

nonlinearities

large-margin objective

cross-view ranking constraints

within-view neighborhood structure preservation constraints

metric learning literature

two-branch neural network

accuracy

image-to-text and text-to-image retrieval

phrase lo-calization

Flickr30K and MSCOCO image-sentence datasets

Flickr30K Entities dataset

method

joint embed-dings of images and text

two-branch neural network

method

large-margin objective

network

Flickr30K Entities dataset

phrase lo-calization

multiple layers of linear projections

two-branch neural network

nonlinearities

two-branch neural network

multiple layers of linear projections

nonlinearities

cross-view ranking constraints

within-view neighborhood structure preservation constraints

cross-view ranking constraints

large-margin objective

within-view neighborhood structure preservation constraints

large-margin objective

accuracy

approach

image-to-text and text-to-image retrieval

approach

Flickr30K and MSCOCO image-sentence datasets

method

phrase lo-calization

method


Input: State-of-the-art Question Answering -LRB- QA -RRB- systems are very sensitive to variations in the phrasing of an information need. Finding the preferred language for such a need is a valuable task. We investigate that claim by adopting a simple MT-based paraphrasing technique and evaluating QA system performance on paraphrased questions. We found a potential increase of 35% in MRR with respect to the original question.

Question Answering -LRB- QA -RRB- systems

MT-based paraphrasing technique

QA system

paraphrased questions

MRR

MT-based paraphrasing technique

QA system

paraphrased questions

QA system


Input: The TAP-XL Automated Analyst's Assistant is an application designed to help an English-speaking analyst write a topical report, culling information from a large inflow of multilingual, multimedia data. It gives users the ability to spend their time finding more data relevant to their task, and gives them translingual reach into other languages by leveraging human language technology.

TAP-XL Automated Analyst's Assistant

multilingual, multimedia data

It

human language technology

multilingual, multimedia data

TAP-XL Automated Analyst's Assistant

human language technology

It


Input: This paper discusses the application of Unification Categorial Grammar -LRB- UCG -RRB- to the framework of Isomorphic Grammars for Machine Translation pioneered by Landsbergen. The Isomorphic Grammars approach to MT involves developing the grammars of the Source and Target languages in parallel, in order to ensure that SL and TL expressions which stand in the translation relation have isomorphic derivations. The principle advantage of this approach is that knowledge concerning translation equivalence of expressions may be directly exploited, obviating the need for answers to semantic questions that we do not yet have. Semantic and other information may still be incorporated, but as constraints on the translation relation, not as levels of textual representation. After introducing this approach to MT system design, and the basics of monolingual UCG, we will show how the two can be integrated, and present an example from an implemented bi-directional English-Spanish fragment. Finally we will present some outstanding problems with the approach.

Unification Categorial Grammar -LRB- UCG -RRB-

Isomorphic Grammars

Isomorphic Grammars approach

textual representation

Machine Translation

MT

MT system design

monolingual UCG

translation relation

isomorphic derivations

translation relation

bi-directional English-Spanish fragment

approach

two

approach

Unification Categorial Grammar -LRB- UCG -RRB-

Isomorphic Grammars

Isomorphic Grammars

Machine Translation

Isomorphic Grammars approach

MT

approach

MT system design

approach

monolingual UCG

MT system design

monolingual UCG

MT system design

two

monolingual UCG

two


Input: In the security domain a key problem is identifying rare behaviours of interest. Training examples for these behaviours may or may not exist, and if they do exist there will be few examples, quite probably one. We present a novel weakly supervised algorithm that can detect behaviours that either have never before been seen or for which there are few examples. Global context is modelled, allowing the detection of abnormal behaviours that in isolation appear normal. Pragmatic aspects are considered, such that no parameter tuning is required and real time performance is achieved.

security domain

identifying rare behaviours of interest

detection of abnormal behaviours

rare behaviours of interest

behaviours

Global context

Pragmatic aspects

Training examples

behaviours

they

weakly supervised algorithm

parameter tuning

identifying rare behaviours of interest

security domain

Training examples

behaviours

weakly supervised algorithm

behaviours

Global context

detection of abnormal behaviours


Input: We have developed a computational model of the process of describing the layout of an apartment or house, a much-studied discourse task first characterized linguistically by Linde -LRB- 1974 -RRB-. The model is embodied in a program, APT, that can reproduce segments of actual tape-recorded descriptions, using organizational and discourse strategies derived through analysis of our corpus.

computational model

model

program

discourse task

APT

organizational and discourse strategies

computational model

discourse task

organizational and discourse strategies

APT

model

program


Input: This paper proposes a practical approach employing n-gram models and error-correction rules for Thai key prediction and Thai-English language identification. The paper also proposes rule-reduction algorithm applying mutual information to reduce the error-correction rules. Our algorithm reported more than 99% accuracy in both language identification and key prediction.

approach

algorithm

n-gram models

error-correction rules

rule-reduction algorithm

Thai key prediction

Thai-English language identification

language identification

key prediction

mutual information

error-correction rules

accuracy

approach

Thai key prediction

approach

Thai-English language identification

n-gram models

approach

error-correction rules

approach

mutual information

rule-reduction algorithm

mutual information

error-correction rules

algorithm

language identification

algorithm

key prediction

n-gram models

error-correction rules

Thai key prediction

Thai-English language identification

accuracy

algorithm


Input: This paper concerns the discourse understanding process in spoken dialogue systems. This process enables the system to understand user utterances based on the context of a dialogue. Since multiple candidates for the understanding result can be obtained for a user utterance due to the ambiguity of speech understanding, it is not appropriate to decide on a single understanding result after each user utterance. By holding multiple candidates for understanding results and resolving the ambiguity as the dialogue progresses, the discourse understanding accuracy can be improved. This paper proposes a method for resolving this ambiguity based on statistical information obtained from dialogue corpora. Unlike conventional methods that use hand-crafted rules, the proposed method enables easy design of the discourse understanding process. Experiment results have shown that a system that exploits the proposed method performs sufficiently and that holding multiple candidates for understanding results is effective.

discourse understanding process

discourse understanding process

spoken dialogue systems

system

ambiguity

method

ambiguity

methods

method

system

method

user utterances

user utterance

dialogue corpora

ambiguity of speech understanding

user utterance

statistical information

hand-crafted rules

discourse understanding accuracy

discourse understanding process

spoken dialogue systems

system

user utterances

method

ambiguity

statistical information

method

dialogue corpora

statistical information

hand-crafted rules

methods

system

method


Input: We consider the problem of question-focused sentence retrieval from complex news articles describing multi-event stories published over time. Annotators generated a list of questions central to understanding each story in our corpus. Because of the dynamic nature of the stories, many questions are time-sensitive -LRB- e.g. ``How many victims have been found?'' -RRB-. Judges found sentences providing an answer to each question. To address the sentence retrieval problem, we apply a stochastic, graph-based method for comparing the relative importance of the textual units, which was previously used successfully for generic summarization. Currently, we present a topic-sensitive version of our method and hypothesize that it can outperform a competitive baseline, which compares the similarity of each sentence to the input question via IDF-weighted word overlap. In our experiments, the method achieves a TRDR score that is significantly higher than that of the baseline.

question-focused sentence retrieval

sentence retrieval problem

generic summarization

news articles

multi-event stories

stochastic, graph-based method

method

it

baseline

method

baseline

IDF-weighted word overlap

TRDR score

news articles

question-focused sentence retrieval

stochastic, graph-based method

sentence retrieval problem

stochastic, graph-based method

generic summarization

multi-event stories

news articles

baseline

it

method

baseline

TRDR score

method

TRDR score

baseline


Input: This paper proposes that sentence analysis should be treated as defeasible reasoning, and presents such a treatment for Japanese sentence analyses using an argumentation system by Konolige, which is a formalization of defeasible reasoning, that includes arguments and defeat rules that capture defeasibility.

sentence analysis

Japanese sentence analyses

defeasible reasoning

argumentation system

formalization of defeasible reasoning

defeasible reasoning

treatment

arguments

defeat rules

defeasibility

defeasible reasoning

sentence analysis

treatment

Japanese sentence analyses

argumentation system

Japanese sentence analyses

arguments

formalization of defeasible reasoning

defeat rules

formalization of defeasible reasoning

arguments

defeat rules

defeasibility

arguments

defeasibility

defeat rules


Input: This paper presents a research on the Czech talking head system. It gives an overview of methods used for visual speech animation, parameterization of a human face and a tongue, necessary data sources and a synthesis method. A 3D animation model is used for a pseudo-muscular animation schema to create such animation of visual speech which is usable for a lipreading. An extension of animation schema is presented to reach more precise deformations mainly in a lip area. Furthermore, a problem of forming articulatory trajectories is formulated to solve labial coarticulation effects. It is used for the synthesis method based on a selection of articulatory targets and interpolation technique.

Czech talking head system

visual speech animation

animation of visual speech

lipreading

forming articulatory trajectories

methods

It

synthesis method

3D animation model

pseudo-muscular animation schema

animation schema

synthesis method

interpolation technique

labial coarticulation effects

selection of articulatory targets

methods

visual speech animation

3D animation model

pseudo-muscular animation schema

pseudo-muscular animation schema

animation of visual speech

animation of visual speech

lipreading

forming articulatory trajectories

labial coarticulation effects

It

synthesis method

selection of articulatory targets

It

interpolation technique

It

selection of articulatory targets

interpolation technique


Input: It is often assumed that when natural language processing meets the real world, the ideal of aiming for complete and correct interpretations has to be abandoned. However, our experience with TACITUS; especially in the MUC-3 evaluation, has shown that principled techniques for syntactic and pragmatic analysis can be bolstered with methods for achieving robustness. We describe three techniques for making syntactic analysis more robust -- an agenda-based scheduling parser, a recovery technique for failed parses, and a new technique called terminal substring parsing. For pragmatics processing, we describe how the method of abductive inference is inherently robust, in that an interpretation is always possible, so that in the absence of the required world knowledge, performance degrades gracefully. Each of these techniques have been evaluated and the results of the evaluations are presented.

natural language processing

syntactic and pragmatic analysis

syntactic analysis

pragmatics processing

TACITUS

agenda-based scheduling parser

recovery technique

terminal substring parsing

abductive inference

MUC-3 evaluation

robustness

techniques

methods

three techniques

technique

these techniques

failed parses

world knowledge

techniques

syntactic and pragmatic analysis

three techniques

syntactic analysis

recovery technique

syntactic analysis

recovery technique

failed parses

abductive inference

pragmatics processing

robustness

methods

agenda-based scheduling parser

three techniques

recovery technique

three techniques

technique

three techniques

agenda-based scheduling parser

recovery technique

recovery technique

technique


Input: This paper proposes a Hidden Markov Model -LRB- HMM -RRB- and an HMM-based chunk tagger, from which a named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system is built to recognize and classify names, times and numerical quantities. Through the HMM, our system is able to apply and integrate four types of internal and external evidences: 1 -RRB- simple deterministic internal feature of the words, such as capitalization and digitalization; 2 -RRB- internal semantic feature of important triggers; 3 -RRB- internal gazetteer feature; 4 -RRB- external macro context feature. In this way, the NER problem can be resolved effectively. Evaluation of our system on MUC-6 and MUC-7 English NE tasks achieves F-measures of 96.6% and 94.1% respectively. It shows that the performance is significantly better than reported by any other machine-learning system. Moreover, the performance is even consistently better than those based on handcrafted rules.

Hidden Markov Model -LRB- HMM -RRB-

HMM-based chunk tagger

named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system

HMM

machine-learning system

names

times and numerical quantities

deterministic internal feature of the words

capitalization

digitalization

internal gazetteer feature

external macro context feature

handcrafted rules

NER problem

system

MUC-6 and MUC-7 English NE tasks

F-measures

Hidden Markov Model -LRB- HMM -RRB-

HMM-based chunk tagger

names

times and numerical quantities

capitalization

digitalization

Hidden Markov Model -LRB- HMM -RRB-

named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system

HMM-based chunk tagger

named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system

named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system

names

named entity -LRB- NE -RRB- recognition -LRB- NER -RRB- system

times and numerical quantities

capitalization

deterministic internal feature of the words

digitalization

deterministic internal feature of the words

MUC-6 and MUC-7 English NE tasks

system

F-measures

system


Input: Two themes have evolved in speech and text image processing work at Xerox PARC that expand and redefine the role of recognition technology in document-oriented applications. One is the development of systems that provide functionality similar to that of text processors but operate directly on audio and scanned image data. A second, related theme is the use of speech and text-image recognition to retrieve arbitrary, user-specified information from documents with signal content. This paper discusses three research initiatives at PARC that exemplify these themes: a text-image editor -LSB- 1 -RSB-, a wordspotter for voice editing and indexing -LSB- 12 -RSB-, and a decoding framework for scanned-document content retrieval -LSB- 4 -RSB-. The discussion focuses on key concepts embodied in the research that enable novel signal-based document processing functionality.

themes

One

systems

theme

research

themes

speech and text image processing

document-oriented applications

voice editing and indexing

scanned-document content retrieval

signal-based document processing functionality

recognition technology

text processors

speech and text-image recognition

text-image editor

wordspotter

decoding framework

audio and scanned image data

documents with signal content

themes

speech and text image processing

recognition technology

document-oriented applications

audio and scanned image data

systems

speech and text-image recognition

theme

documents with signal content

speech and text-image recognition

decoding framework

scanned-document content retrieval

systems

text processors

text-image editor

wordspotter

wordspotter

voice editing and indexing

voice editing and indexing

decoding framework

text-image editor

research

wordspotter

research

decoding framework

research


Input: The problem of predicting image or video interestingness from their low-level feature representations has received increasing interest. As a highly subjective visual attribute, annotating the interesting-ness value of training data for learning a prediction model is challenging. To make the annotation less subjective and more reliable, recent studies employ crowdsourcing tools to collect pairwise comparisons -- relying on majority voting to prune the annotation outliers/errors. In this paper, we propose a more principled way to identify annotation outliers by formulating the interestingness prediction task as a unified robust learning to rank problem, tackling both the outlier detection and interestingness prediction tasks jointly. Extensive experiments on both image and video interestingness benchmark datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives.

predicting image or video interestingness

interestingness prediction task

rank problem

outlier detection

interestingness prediction tasks

low-level feature representations

prediction model

crowdsourcing tools

majority voting

unified robust learning

subjective visual attribute

interesting-ness value

pairwise comparisons

annotation outliers/errors

annotation outliers

way

approach

state-of-the-art alternatives

image and video interestingness benchmark datasets

low-level feature representations

predicting image or video interestingness

crowdsourcing tools

pairwise comparisons

majority voting

crowdsourcing tools

majority voting

annotation outliers/errors

way

annotation outliers

way

outlier detection

way

interestingness prediction tasks

interestingness prediction task

annotation outliers

unified robust learning

interestingness prediction task

unified robust learning

rank problem

outlier detection

interestingness prediction tasks

image and video interestingness benchmark datasets

approach

approach

state-of-the-art alternatives


Input: Many description logics -LRB- DLs -RRB- combine knowledge representation on an abstract, logical level with an interface to ``concrete'' domains such as numbers and strings. We propose to extend such DLs with key constraints that allow the expression of statements like ``US citizens are uniquely identified by their social security number''. Based on this idea, we introduce a number of natural description logics and present -LRB- un -RRB- decidability results and tight NEx-PTlME complexity bounds.

description logics -LRB- DLs -RRB-

knowledge representation

DLs

natural description logics

tight NEx-PTlME complexity bounds

knowledge representation

description logics -LRB- DLs -RRB-


Input: We describe an implementation of data-driven selection of emphatic facial displays for an embodied conversational agent in a dialogue system. A corpus of sentences in the domain of the target dialogue system was recorded, and the facial displays used by the speaker were annotated. The data from those recordings was used in a range of models for generating facial displays, each model making use of a different amount of context or choosing displays differently within a context. The models were evaluated in two ways: by cross-validation against the corpus, and by asking users to rate the output. The predictions of the cross-validation study differed from the actual user ratings. While the cross-validation gave the highest scores to models making a majority choice within a context, the user study showed a significant preference for models that produced more variation. This preference was especially strong among the female subjects.

data-driven selection

embodied conversational agent

dialogue system

emphatic facial displays

corpus of sentences

facial displays

facial displays

dialogue system

cross-validation

cross-validation

cross-validation

data

models

model

models

corpus

data-driven selection

embodied conversational agent

emphatic facial displays

data-driven selection

data

models

models

facial displays

embodied conversational agent

dialogue system

cross-validation

models


Input: When classifying high-dimensional sequence data, traditional methods -LRB- e.g., HMMs, CRFs -RRB- may require large amounts of training data to avoid overfitting. In such cases dimensionality reduction can be employed to find a low-dimensional representation on which classification can be done more efficiently. Existing methods for supervised dimensionality reduction often presume that the data is densely sampled so that a neighborhood graph structure can be formed, or that the data arises from a known distribution. Sufficient dimension reduction techniques aim to find a low dimensional representation such that the remaining degrees of freedom become conditionally independent of the output values. In this paper we develop a novel sequence kernel dimension reduction approach -LRB- S-KDR -RRB-. Our approach does not make strong assumptions on the distribution of the input data. Spatial, temporal and periodic information is combined in a principled manner, and an optimal manifold is learned for the end-task. We demonstrate the effectiveness of our approach on several tasks involving the discrimination of human gesture and motion categories, as well as on a database of dynamic textures.

classifying high-dimensional sequence data

classification

supervised dimensionality reduction

discrimination of human gesture and motion categories

HMMs

CRFs

dimensionality reduction

low-dimensional representation

Sufficient dimension reduction techniques

low dimensional representation

sequence kernel dimension reduction approach -LRB- S-KDR -RRB-

overfitting

neighborhood graph structure

known distribution

Spatial, temporal and periodic information

manifold

Existing methods

approach

end-task

approach

database of dynamic textures

HMMs

classifying high-dimensional sequence data

CRFs

classifying high-dimensional sequence data

dimensionality reduction

low-dimensional representation

low-dimensional representation

classification

Existing methods

supervised dimensionality reduction

Sufficient dimension reduction techniques

low dimensional representation

manifold

end-task

HMMs

CRFs

discrimination of human gesture and motion categories

approach

database of dynamic textures

approach


Input: We present an efficient algorithm for chart-based phrase structure parsing of natural language that is tailored to the problem of extracting specific information from unrestricted texts where many of the words are unknown and much of the text is irrelevant to the task. The parser gains algorithmic efficiency through a reduction of its search space. As each new edge is added to the chart, the algorithm checks only the topmost of the edges adjacent to it, rather than all such edges as in conventional treatments. The resulting spanning edges are insured to be the correct ones by carefully controlling the order in which edges are introduced so that every final constituent covers the longest possible span. This is facilitated through the use of phrase boundary heuristics based on the placement of function words, and by heuristic rules that permit certain kinds of phrases to be deduced despite the presence of unknown words. A further reduction in the search space is achieved by using semantic rather than syntactic categories on the terminal and non-terminal edges, thereby reducing the amount of ambiguity and thus the number of edges, since only edges with a valid semantic interpretation are ever introduced.

algorithm

chart-based phrase structure parsing

natural language

parser

phrase boundary heuristics

heuristic rules

search space

edge

chart

edges

edges

spanning edges

edges

function words

unknown words

reduction in the search space

semantic

syntactic categories

terminal and non-terminal edges

edges

edges

algorithm

chart-based phrase structure parsing

natural language

chart-based phrase structure parsing

function words

phrase boundary heuristics

semantic

reduction in the search space

semantic

syntactic categories

semantic

terminal and non-terminal edges

syntactic categories

terminal and non-terminal edges


Input: Automatic estimation of word significance oriented for speech-based Information Retrieval -LRB- IR -RRB- is addressed. Since the significance of words differs in IR, automatic speech recognition -LRB- ASR -RRB- performance has been evaluated based on weighted word error rate -LRB- WWER -RRB-, which gives a weight on errors from the viewpoint of IR, instead of word error rate -LRB- WER -RRB-, which treats all words uniformly. A decoding strategy that minimizes WWER based on a Minimum Bayes-Risk framework has been shown, and the reduction of errors on both ASR and IR has been reported. In this paper, we propose an automatic estimation method for word significance -LRB- weights -RRB- based on its influence on IR. Specifically, weights are estimated so that evaluation measures of ASR and IR are equivalent. We apply the proposed method to a speech-based information retrieval system, which is a typical IR system, and show that the method works well.

Automatic estimation of word significance

speech-based Information Retrieval -LRB- IR -RRB-

IR

automatic speech recognition -LRB- ASR -RRB-

IR

ASR

IR

IR

ASR

IR

speech-based information retrieval system

IR system

word significance

significance

word significance -LRB- weights -RRB-

weighted word error rate -LRB- WWER -RRB-

word error rate -LRB- WER -RRB-

WWER

evaluation measures

decoding strategy

Minimum Bayes-Risk framework

automatic estimation method

method

method

Automatic estimation of word significance

speech-based Information Retrieval -LRB- IR -RRB-

decoding strategy

WWER

Minimum Bayes-Risk framework

decoding strategy

method

speech-based information retrieval system

weighted word error rate -LRB- WWER -RRB-

automatic speech recognition -LRB- ASR -RRB-

automatic estimation method

word significance -LRB- weights -RRB-

evaluation measures

ASR

evaluation measures

IR

word error rate -LRB- WER -RRB-

weighted word error rate -LRB- WWER -RRB-

ASR

IR

ASR

IR

speech-based information retrieval system

IR system


Input: Methods developed for spelling correction for languages like English -LRB- see the review by Kukich -LRB- Kukich, 1992 -RRB- -RRB- are not readily applicable to agglutinative languages. This poster presents an approach to spelling correction in agglutinative languages that is based on two-level morphology and a dynamic-programming based search algorithm. After an overview of our approach, we present results from experiments with spelling correction in Turkish.

Methods

approach

approach

spelling correction

spelling correction

spelling correction

languages

English

agglutinative languages

agglutinative languages

two-level morphology

Turkish

dynamic-programming based search algorithm

Methods

spelling correction

spelling correction

languages

approach

spelling correction

agglutinative languages

spelling correction

two-level morphology

spelling correction

dynamic-programming based search algorithm

spelling correction

Turkish

spelling correction

English

languages

two-level morphology

dynamic-programming based search algorithm


Input: In this paper, we present a novel training method for a localized phrase-based prediction model for statistical machine translation -LRB- SMT -RRB-. The model predicts blocks with orientation to handle local phrase re-ordering. We use a maximum likelihood criterion to train a log-linear block bigram model which uses real-valued features -LRB- e.g. a language model score -RRB- as well as binary features based on the block identities themselves, e.g. block bigram features. Our training algorithm can easily handle millions of features. The best system obtains a 18.6% improvement over the baseline on a standard Arabic-English translation task.

training method

localized phrase-based prediction model

log-linear block bigram model

training algorithm

statistical machine translation -LRB- SMT -RRB-

local phrase re-ordering

Arabic-English translation task

model

system

baseline

maximum likelihood criterion

real-valued features

language model score

binary features

features

training method

localized phrase-based prediction model

localized phrase-based prediction model

statistical machine translation -LRB- SMT -RRB-

model

local phrase re-ordering

maximum likelihood criterion

log-linear block bigram model

real-valued features

log-linear block bigram model

binary features

log-linear block bigram model

features

training algorithm

real-valued features

binary features

language model score

real-valued features

system

baseline

Arabic-English translation task

system

Arabic-English translation task

baseline


Input: In this paper we describe a novel data structure for phrase-based statistical machine translation which allows for the retrieval of arbitrarily long phrases while simultaneously using less memory than is required by current decoder implementations. We detail the computational complexity and average retrieval times for looking up phrase translations in our suffix array-based data structure. We show how sampling can be used to reduce the retrieval time by orders of magnitude with no loss in translation quality.

data structure

memory

phrase translations

suffix array-based data structure

phrase-based statistical machine translation

retrieval of arbitrarily long phrases

decoder

sampling

computational complexity

average retrieval times

retrieval time

translation quality

data structure

phrase-based statistical machine translation

data structure

retrieval of arbitrarily long phrases

computational complexity

average retrieval times

phrase translations

suffix array-based data structure

retrieval time

sampling

translation quality

sampling


Input: The major objective of this program is to develop and demonstrate robust, high performance continuous speech recognition -LRB- CSR -RRB- techniques focussed on application in Spoken Language Systems -LRB- SLS -RRB- which will enhance the effectiveness of military and civilian computer-based systems. A key complementary objective is to define and develop applications of robust speech recognition and understanding systems, and to help catalyze the transition of spoken language technology into military and civilian systems, with particular focus on application of robust CSR to mobile military command and control. The research effort focusses on developing advanced acoustic modelling, rapid search, and recognition-time adaptation techniques for robust large-vocabulary CSR, and on applying these techniques to the new ARPA large-vocabulary CSR corpora and to military application tasks.

continuous speech recognition -LRB- CSR -RRB- techniques

spoken language technology

CSR

acoustic modelling

rapid search

recognition-time adaptation techniques

large-vocabulary CSR

Spoken Language Systems -LRB- SLS -RRB-

military and civilian computer-based systems

speech recognition and understanding systems

military and civilian systems

mobile military command and control

military application tasks

techniques

ARPA large-vocabulary CSR corpora

continuous speech recognition -LRB- CSR -RRB- techniques

Spoken Language Systems -LRB- SLS -RRB-

Spoken Language Systems -LRB- SLS -RRB-

military and civilian computer-based systems

spoken language technology

military and civilian systems

CSR

mobile military command and control

acoustic modelling

large-vocabulary CSR

acoustic modelling

military application tasks

rapid search

large-vocabulary CSR

rapid search

military application tasks

recognition-time adaptation techniques

large-vocabulary CSR

recognition-time adaptation techniques

military application tasks

large-vocabulary CSR

ARPA large-vocabulary CSR corpora

techniques

military application tasks

acoustic modelling

rapid search

rapid search

recognition-time adaptation techniques

acoustic modelling

techniques

rapid search

techniques

recognition-time adaptation techniques

techniques

ARPA large-vocabulary CSR corpora

techniques


Input: This paper examines what kind of similarity between words can be represented by what kind of word vectors in the vector space model. Through two experiments, three methods for constructing word vectors, i.e., LSA-based, cooccurrence-based and dictionary-based methods, were compared in terms of the ability to represent two kinds of similarity, i.e., taxonomic similarity and associative similarity. The result of the comparison was that the dictionary-based word vectors better reflect taxonomic similarity, while the LSA-based and the cooccurrence-based word vectors better reflect associative similarity.

similarity between words

word vectors

taxonomic similarity

associative similarity

dictionary-based word vectors

taxonomic similarity

LSA-based and the cooccurrence-based word vectors

associative similarity

vector space model

LSA-based, cooccurrence-based and dictionary-based methods

methods

similarity

constructing word vectors

word vectors

similarity between words

vector space model

word vectors

methods

constructing word vectors

LSA-based, cooccurrence-based and dictionary-based methods

similarity

dictionary-based word vectors

taxonomic similarity

LSA-based and the cooccurrence-based word vectors

associative similarity

taxonomic similarity

similarity

associative similarity

similarity

taxonomic similarity

associative similarity


Input: This paper presents a maximum entropy word alignment algorithm for Arabic-English based on supervised training data. We demonstrate that it is feasible to create training material for problems in machine translation and that a mixture of supervised and unsupervised methods yields superior performance. The probabilistic model used in the alignment directly models the link decisions. Significant improvement over traditional word alignment techniques is shown as well as improvement on several machine translation tests. Performance of the algorithm is contrasted with human annotation performance.

maximum entropy word alignment algorithm

supervised and unsupervised methods

probabilistic model

word alignment techniques

Arabic-English

supervised training data

training material

machine translation

alignment

link decisions

machine translation tests

human annotation

algorithm

Arabic-English

maximum entropy word alignment algorithm

supervised training data

maximum entropy word alignment algorithm

training material

machine translation

probabilistic model

alignment

probabilistic model

link decisions

word alignment techniques

machine translation tests

algorithm

human annotation


Input: In this paper, we propose a novel Cooperative Model for natural language understanding in a dialogue system. We build this based on both Finite State Model -LRB- FSM -RRB- and Statistical Learning Model -LRB- SLM -RRB-. FSM provides two strategies for language understanding and have a high accuracy but little robustness and flexibility. Statistical approach is much more robust but less accurate. Cooperative Model incorporates all the three strategies together and thus can suppress all the shortcomings of different strategies and has all the advantages of the three strategies.

Cooperative Model

Finite State Model -LRB- FSM -RRB-

Statistical Learning Model -LRB- SLM -RRB-

FSM

Statistical approach

Cooperative Model

natural language understanding

dialogue system

language understanding

this

Cooperative Model

natural language understanding

natural language understanding

dialogue system

Finite State Model -LRB- FSM -RRB-

this

Statistical Learning Model -LRB- SLM -RRB-

this

FSM

language understanding

Finite State Model -LRB- FSM -RRB-

Statistical Learning Model -LRB- SLM -RRB-


Input: In the Chinese language, a verb may have its dependents on its left, right or on both sides. The ambiguity resolution of right-side dependencies is essential for dependency parsing of sentences with two or more verbs. Previous works on shift-reduce dependency parsers may not guarantee the connectivity of a dependency tree due to their weakness at resolving the right-side dependencies. This paper proposes a two-phase shift-reduce dependency parser based on SVM learning. The left-side dependents and right-side nominal dependents are detected in Phase I, and right-side verbal dependents are decided in Phase II. In experimental evaluation, our proposed method outperforms previous shift-reduce dependency parsers for the Chine language, showing improvement of dependency accuracy by 10.08%.

Chinese language

Chine language

ambiguity resolution of right-side dependencies

dependency parsing

shift-reduce dependency parsers

two-phase shift-reduce dependency parser

SVM learning

shift-reduce dependency parsers

connectivity

dependency accuracy

dependency tree

right-side dependencies

left-side dependents

right-side nominal dependents

right-side verbal dependents

method

ambiguity resolution of right-side dependencies

dependency parsing

SVM learning

two-phase shift-reduce dependency parser

connectivity

dependency tree

Chine language

method

Chine language

shift-reduce dependency parsers

dependency accuracy

method

dependency accuracy

shift-reduce dependency parsers

left-side dependents

right-side nominal dependents

right-side verbal dependents

right-side nominal dependents

method

shift-reduce dependency parsers


Input: In order to meet the needs of a publication of papers in English, many systems to run off texts have been developed. In this paper, we report a system FROFF which can make a fair copy of not only texts but also graphs and tables indispensable to our papers. Its selection of fonts, specification of character size are dynamically changeable, and the typing location can be also changed in lateral or longitudinal directions. Each character has its own width and a line length is counted by the sum of each character. By using commands or rules which are defined to facilitate the construction of format expected or some mathematical expressions, elaborate and pretty documents can be successfully obtained.

system

FROFF

typing location

commands

rules

mathematical expressions

commands

rules

commands

mathematical expressions

rules

mathematical expressions


Input: This paper presents an evaluation method employing a latent variable model for paraphrases with their contexts. We assume that the context of a sentence is indicated by a latent variable of the model as a topic and that the likelihood of each variable can be inferred. A paraphrase is evaluated for whether its sentences are used in the same context. Experimental results showed that the proposed method achieves almost 60% accuracy and that there is not a large performance difference between the two models. The results also revealed an upper bound of accuracy of 77% with the method when using only topic information.

evaluation method

model

method

method

latent variable model

paraphrases

latent variable

paraphrase

topic information

accuracy

evaluation method

paraphrases

accuracy

method

latent variable model

evaluation method

topic information

method


Input: We describe the methods and hardware that we are using to produce a real-time demonstration of an integrated Spoken Language System. We describe algorithms that greatly reduce the computation needed to compute the N-Best sentence hypotheses. To avoid grammar coverage problems we use a fully-connected first-order statistical class grammar. The speech-search algorithm is implemented on a board with a single Intel i860 chip, which provides a factor of 5 speed-up over a SUN 4 for straight C code. The board plugs directly into the VME bus of the SUN4, which controls the system and contains the natural language system and application back end.

methods

hardware

algorithms

system

integrated Spoken Language System

fully-connected first-order statistical class grammar

speech-search algorithm

natural language system

N-Best sentence hypotheses

grammar coverage problems

board

Intel i860 chip

SUN 4

straight C code

board

VME bus

application back end

methods

hardware

natural language system

application back end

methods

integrated Spoken Language System

hardware

integrated Spoken Language System

algorithms

N-Best sentence hypotheses

fully-connected first-order statistical class grammar

grammar coverage problems

board

speech-search algorithm

Intel i860 chip

straight C code

SUN 4

straight C code

board

system

Intel i860 chip

board

VME bus

natural language system

board

application back end

board

Intel i860 chip

SUN 4


Input: We address the problem of estimating location information of an image using principles from automated representation learning. We pursue a hierarchical sparse coding approach that learns features useful in discriminating images across locations, by initializing it with a geometric prior corresponding to transformations between image appearance space and their corresponding location grouping space using the notion of parallel transport on manifolds. We then extend this approach to account for the availability of heterogeneous data modalities such as geo-tags and videos pertaining to different locations, and also study a relatively under-addressed problem of transferring knowledge available from certain locations to infer the grouping of data from novel locations. We evaluate our approach on several standard datasets such as im2gps, San Francisco and MediaEval2010, and obtain state-of-the-art results.

estimating location information

transferring knowledge

grouping of data

image

San Francisco

automated representation learning

hierarchical sparse coding approach

approach

features

geometric prior

image appearance space

location grouping space

parallel transport on manifolds

heterogeneous data modalities

geo-tags

videos

it

approach

datasets

image

estimating location information

automated representation learning

estimating location information

geometric prior

it

parallel transport on manifolds

geometric prior

approach

heterogeneous data modalities

transferring knowledge

grouping of data

geo-tags

heterogeneous data modalities

videos

heterogeneous data modalities

datasets

San Francisco

datasets

datasets

geo-tags

videos

San Francisco

San Francisco

datasets

approach


Input: Conventional HMMs have weak duration constraints. In noisy conditions, the mismatch between corrupted speech signals and models trained on clean speech may cause the decoder to produce word matches with unrealistic durations. This paper presents a simple way to incorporate word duration constraints by unrolling HMMs to form a lattice where word duration probabilities can be applied directly to state transitions. The expanded HMMs are compatible with conventional Viterbi decoding. Experiments on connected-digit recognition show that when using explicit duration constraints the decoder generates word matches with more reasonable durations, and word error rates are significantly reduced across a broad range of noise conditions.

HMMs

decoder

unrolling HMMs

HMMs

Viterbi decoding

decoder

weak duration constraints

word matches

unrealistic durations

word duration constraints

lattice

word duration probabilities

state transitions

duration constraints

word matches

noise conditions

corrupted speech signals

clean speech

models

connected-digit recognition

word error rates

weak duration constraints

HMMs

unrealistic durations

word matches

clean speech

models

decoder

word matches

unrolling HMMs

word duration constraints

unrolling HMMs

lattice

word duration probabilities

state transitions

connected-digit recognition

decoder

duration constraints

decoder

decoder

word matches

Viterbi decoding

HMMs


Input: Soames 1979 provides some counterexamples to the theory of natural language presuppositions that is presented in Gazdar 1979. Soames 1982 provides a theory which explains these counterexamples. Mercer 1987 rejects the solution found in Soames 1982 leaving these counterexamples unexplained. By reappraising these insightful counterexamples, the inferential theory for natural language presuppositions described in Mercer 1987, 1988 gives a simple and straightforward explanation for the presuppositional nature of these sentences.

natural language presuppositions

natural language presuppositions

presuppositional nature


Input: One of the claimed benefits of Tree Adjoining Grammars is that they have an extended domain of locality -LRB- EDOL -RRB-. We consider how this can be exploited to limit the need for feature structure unification during parsing. We compare two wide-coverage lexicalized grammars of English, LEXSYS and XTAG, finding that the two grammars exploit EDOL in different ways.

Tree Adjoining Grammars

feature structure unification

lexicalized grammars of English

LEXSYS

XTAG

grammars

they

extended domain of locality -LRB- EDOL -RRB-

EDOL

parsing

Tree Adjoining Grammars

extended domain of locality -LRB- EDOL -RRB-

feature structure unification

parsing

grammars

EDOL

LEXSYS

lexicalized grammars of English

XTAG

lexicalized grammars of English

LEXSYS

XTAG


Input: Identity uncertainty is a pervasive problem in real-world data analysis. It arises whenever objects are not labeled with unique identifiers or when those identifiers may not be perceived perfectly. In such cases, two observations may or may not correspond to the same object. In this paper, we consider the problem in the context of citation matching -- the problem of deciding which citations correspond to the same publication. Our approach is based on the use of a relational probability model to define a generative model for the domain, including models of author and title corruption and a probabilistic citation grammar. Identity uncertainty is handled by extending standard models to incorporate probabilities over the possible mappings between terms in the language and objects in the domain. Inference is based on Markov chain Monte Carlo, augmented with specific methods for generating efficient proposals when the domain contains many objects. Results on several citation data sets show that the method outperforms current algorithms for citation matching. The declarative, relational nature of the model also means that our algorithm can determine object characteristics such as author names by combining multiple citations of multiple papers.

Identity uncertainty

real-world data analysis

citation matching

Identity uncertainty

Inference

citation matching

identifiers

identifiers

problem

approach

domain

models

methods

method

model

algorithm

citations

publication

mappings

domain

object characteristics

author names

citations

relational probability model

generative model

models of author and title corruption

probabilistic citation grammar

Markov chain Monte Carlo

current algorithms

citation data sets

Identity uncertainty

real-world data analysis

author names

object characteristics

relational probability model

approach

relational probability model

generative model

generative model

domain

models

Identity uncertainty

Markov chain Monte Carlo

Inference

methods

Inference

method

citation matching

current algorithms

citation matching

algorithm

object characteristics

models of author and title corruption

relational probability model

probabilistic citation grammar

relational probability model

models of author and title corruption

probabilistic citation grammar

citation data sets

method

method

current algorithms


Input: The paper proposes and empirically motivates an integration of supervised learning with unsupervised learning to deal with human biases in summarization. In particular, we explore the use of probabilistic decision tree within the clustering framework to account for the variation as well as regularity in human created summaries. The corpus of human created extracts is created from a newspaper corpus and used as a test set. We build probabilistic decision trees of different flavors and integrate each of them with the clustering framework. Experiments with the corpus demonstrate that the mixture of the two paradigms generally gives a significant boost in performance compared to cases where either ofthe two is considered alone.

supervised learning

unsupervised learning

clustering framework

clustering framework

human biases in summarization

probabilistic decision tree

probabilistic decision trees

human created summaries

corpus of human created extracts

newspaper corpus

them

corpus

supervised learning

human biases in summarization

unsupervised learning

human biases in summarization

newspaper corpus

corpus of human created extracts

unsupervised learning

supervised learning

clustering framework

them

clustering framework

probabilistic decision tree


Input: In this study, we propose a knowledge-independent method for aligning terms and thus extracting translations from a small, domain-specific corpus consisting of parallel English and Chinese court judgments from Hong Kong. With a sentence-aligned corpus, translation equivalences are suggested by analysing the frequency profiles of parallel concordances. The method overcomes the limitations of conventional statistical methods which require large corpora to be effective, and lexical approaches which depend on existing bilingual dictionaries. Pilot testing on a parallel corpus of about 113K Chinese words and 120K English words gives an encouraging 85% precision and 45% recall. Future work includes fine-tuning the algorithm upon the analysis of the errors, and acquiring a translation lexicon for legal terminology by filtering out general terms.

knowledge-independent method

statistical methods

lexical approaches

small, domain-specific corpus

parallel English and Chinese court judgments

sentence-aligned corpus

large corpora

bilingual dictionaries

parallel corpus

legal terminology

translation equivalences

parallel concordances

translation lexicon

frequency profiles

method

algorithm

precision

recall

parallel English and Chinese court judgments

small, domain-specific corpus

frequency profiles

parallel concordances

sentence-aligned corpus

translation equivalences

large corpora

statistical methods

bilingual dictionaries

lexical approaches

translation lexicon

legal terminology

method

statistical methods

method

lexical approaches

precision

recall


Input: Sentiment Classification seeks to identify a piece of text according to its author's general feeling toward their subject, be it positive or negative. Traditional machine learning techniques have been applied to this problem with reasonable success, but they have been shown to work well only when there is a good match between the training and test data with respect to topic. This paper demonstrates that match with respect to domain and time is also important, and presents preliminary experiments with training data labeled with emoticons, which has the potential of being independent of domain, topic and time.

Sentiment Classification

machine learning techniques

problem

training and test data

training data

emoticons

machine learning techniques

problem

emoticons

training data


Input: We present a novel algorithm for estimating the broad 3D geometric structure of outdoor video scenes. Leveraging spatio-temporal video segmentation, we decompose a dynamic scene captured by a video into geometric classes, based on predictions made by region-classifiers that are trained on appearance and motion features. By examining the homogeneity of the prediction, we combine predictions across multiple segmentation hierarchy levels alleviating the need to determine the granularity a priori. We built a novel, extensive dataset on geometric context of video to evaluate our method, consisting of over 100 ground-truth annotated outdoor videos with over 20,000 frames. To further scale beyond this dataset, we propose a semi-supervised learning framework to expand the pool of labeled data with high confidence predictions obtained from unlabeled data. Our system produces an accurate prediction of geometric context of video achieving 96% accuracy across main geometric classes.

algorithm

dataset

method

dataset

system

3D geometric structure of outdoor video scenes

spatio-temporal video segmentation

region-classifiers

semi-supervised learning framework

dynamic scene

geometric classes

appearance and motion features

segmentation hierarchy levels

granularity a priori

geometric context of video

annotated outdoor videos

high confidence predictions

unlabeled data

geometric context of video

labeled data

accuracy

algorithm

3D geometric structure of outdoor video scenes

spatio-temporal video segmentation

dynamic scene

region-classifiers

geometric classes

appearance and motion features

region-classifiers

semi-supervised learning framework

labeled data

high confidence predictions

semi-supervised learning framework

unlabeled data

high confidence predictions

system

geometric context of video

geometric classes

dynamic scene

annotated outdoor videos

dataset

dataset

method

accuracy

system

geometric context of video

dataset