Input: English is shown to be trans-context-free on the basis of coordinations of the respectively type that involve strictly syntactic cross-serial agreement. The agreement in question involves number in nouns and reflexive pronouns and is syntactic rather than semantic in nature because grammatical number in English, like grammatical gender in languages such as French, is partly arbitrary. The formal proof, which makes crucial use of the Interchange Lemma of Ogden et al., is so constructed as to be valid even if English is presumed to contain grammatical sentences in which respectively operates across a pair of coordinate phrases one of whose members has fewer conjuncts than the other; it thus goes through whatever the facts may be regarding constructions with unequal numbers of conjuncts in the scope of respectively, whereas other arguments have foundered on this problem.

English

English

languages

French

English

coordinations

strictly syntactic cross-serial agreement

nouns

reflexive pronouns

grammatical number

grammatical gender

agreement

Interchange Lemma

nouns

reflexive pronouns

grammatical gender

languages

French

languages


Input: In this paper, a novel method to learn the intrinsic object structure for robust visual tracking is proposed. The basic assumption is that the parameterized object state lies on a low dimensional manifold and can be learned from training data. Based on this assumption, firstly we derived the dimensionality reduction and density estimation algorithm for unsupervised learning of object intrinsic representation, the obtained non-rigid part of object state reduces even to 2 dimensions. Secondly the dynamical model is derived and trained based on this intrinsic representation. Thirdly the learned intrinsic object structure is integrated into a particle-filter style tracker. We will show that this intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle-filter style tracker more robust and reliable. Experiments show that the learned tracker performs much better than existing trackers on the tracking of complex non-rigid motions such as fish twisting with self-occlusion and large inter-frame lip motion. The proposed method also has the potential to solve other type of tracking problems.

method

dimensionality reduction and density estimation algorithm

object intrinsic representation

dynamical model

intrinsic representation

particle-filter style tracker

intrinsic object representation

dynamical model

particle-filter style tracker

intrinsic object structure

parameterized object state

low dimensional manifold

non-rigid part of object state

intrinsic object structure

complex non-rigid motions

fish twisting

self-occlusion

inter-frame lip motion

robust visual tracking

unsupervised learning of object intrinsic representation

tracking of complex non-rigid motions

tracking problems

tracker

trackers

method

method

intrinsic object structure

intrinsic object structure

robust visual tracking

dimensionality reduction and density estimation algorithm

unsupervised learning of object intrinsic representation

intrinsic representation

dynamical model

dynamical model

particle-filter style tracker

tracker

tracking of complex non-rigid motions

trackers

tracking of complex non-rigid motions

method

tracking problems

low dimensional manifold

parameterized object state

self-occlusion

fish twisting

inter-frame lip motion

fish twisting

intrinsic object structure

particle-filter style tracker

tracker

trackers

fish twisting

complex non-rigid motions

self-occlusion

inter-frame lip motion


Input: In this paper, we present a digital signal processor -LRB- DSP -RRB- implementation of real-time statistical voice conversion -LRB- VC -RRB- for silent speech enhancement and electrolaryngeal speech enhancement. As a silent speech interface, we focus on non-audible murmur -LRB- NAM -RRB-, which can be used in situations where audible speech is not acceptable. Electrolaryngeal speech is one of the typical types of alaryngeal speech produced by an alternative speaking method for laryngectomees. However, the sound quality of NAM and electrolaryngeal speech suffers from lack of naturalness. VC has proven to be one of the promising approaches to address this problem, and it has been successfully implemented on devices with sufficient computational resources. An implementation on devices that are highly portable but have limited computational resources would greatly contribute to its practical use. In this paper we further implement real-time VC on a DSP. To implement the two speech enhancement systems based on real-time VC, one from NAM to a whispered voice and the other from electrolaryngeal speech to a natural voice, we propose several methods for reducing computational cost while preserving conversion accuracy. We conduct experimental evaluations and show that real-time VC is capable of running on a DSP with little degradation.

digital signal processor -LRB- DSP -RRB- implementation

real-time statistical voice conversion -LRB- VC -RRB-

speaking method

VC

real-time VC

speech enhancement systems

real-time VC

real-time VC

silent speech enhancement

electrolaryngeal speech enhancement

silent speech interface

laryngectomees

DSP

DSP

non-audible murmur -LRB- NAM -RRB-

audible speech

Electrolaryngeal speech

alaryngeal speech

NAM

NAM and electrolaryngeal speech

sufficient computational resources

limited computational resources

NAM

whispered voice

electrolaryngeal speech

natural voice

sound quality

computational cost

conversion accuracy

it

devices

devices

one

other

methods

digital signal processor -LRB- DSP -RRB- implementation

real-time statistical voice conversion -LRB- VC -RRB-

real-time statistical voice conversion -LRB- VC -RRB-

silent speech enhancement

real-time statistical voice conversion -LRB- VC -RRB-

electrolaryngeal speech enhancement

speaking method

alaryngeal speech

speaking method

laryngectomees

devices

it

DSP

real-time VC

real-time VC

speech enhancement systems

DSP

real-time VC

silent speech enhancement

electrolaryngeal speech enhancement

one

other

computational cost

conversion accuracy

Electrolaryngeal speech

alaryngeal speech

one

speech enhancement systems

other

speech enhancement systems

sound quality

NAM and electrolaryngeal speech

computational cost

methods

conversion accuracy

methods

sufficient computational resources

devices

limited computational resources

devices


Input: We propose a method that automatically generates paraphrase sets from seed sentences to be used as reference sets in objective machine translation evaluation measures like BLEU and NIST. We measured the quality of the paraphrases produced in an experiment, i.e., -LRB- i -RRB- their grammaticality: at least 99% correct sentences; -LRB- ii -RRB- their equivalence in meaning: at least 96% correct paraphrases either by meaning equivalence or entailment; and, -LRB- iii -RRB- the amount of internal lexical and syntactical variation in a set of paraphrases: slightly superior to that of hand-produced sets. The paraphrase sets produced by this method thus seem adequate as reference sets to be used for MT evaluation.

method

method

paraphrase

paraphrases

paraphrases

paraphrases

hand-produced sets

paraphrase

machine translation evaluation measures

BLEU

NIST

grammaticality

equivalence in meaning

internal lexical and syntactical variation

meaning equivalence

entailment

MT evaluation

method

paraphrase

paraphrase

machine translation evaluation measures

meaning equivalence

paraphrases

entailment

paraphrases

method

paraphrase

BLEU

machine translation evaluation measures

NIST

machine translation evaluation measures

BLEU

NIST

equivalence in meaning

grammaticality

meaning equivalence

entailment

internal lexical and syntactical variation

equivalence in meaning

paraphrases

hand-produced sets


Input: Graph unification remains the most expensive part of unification-based grammar parsing. We focus on one speed-up element in the design of unification algorithms: avoidance of copying of unmodified subgraphs. We propose a method of attaining such a design through a method of structure-sharing which avoids log -LRB- d -RRB- overheads often associated with structure-sharing of graphs without any use of costly dependency pointers. The proposed scheme eliminates redundant copying while maintaining the quasi-destructive scheme's ability to avoid over copying and early copying combined with its ability to handle cyclic structures without algorithmic additions.

Graph unification

unification-based grammar parsing

speed-up element

unification algorithms

structure-sharing

dependency pointers

copying of unmodified subgraphs

unmodified subgraphs

log -LRB- d -RRB- overheads

structure-sharing of graphs

redundant copying

quasi-destructive scheme's ability

over copying

early copying

cyclic structures

method

scheme

Graph unification

unification-based grammar parsing

speed-up element

unification algorithms

structure-sharing

method

scheme

cyclic structures

quasi-destructive scheme's ability

scheme

over copying

early copying


Input: We describe a novel technique and implemented system for constructing a subcategorization dictionary from textual corpora. Each dictionary entry encodes the relative frequency of occurrence of a comprehensive set of subcategorization classes for English. An initial experiment, on a sample of 14 verbs which exhibit multiple complementation patterns, demonstrates that the technique achieves accuracy comparable to previous approaches, which are all limited to a highly restricted set of subcategorization classes. We also demonstrate that a subcategorization dictionary built with the system improves the accuracy of a parser by an appreciable amount

system

system

subcategorization dictionary

relative frequency of occurrence

subcategorization dictionary

textual corpora

accuracy

parser

system

subcategorization dictionary

textual corpora

system

system

subcategorization dictionary

accuracy

parser

parser

subcategorization dictionary


Input: The introduction of prior knowledge has greatly enhanced numerous purely low-level driven image processing algorithms. In this work, we focus on the problem of non-rigid image registration. A number of powerful registration criteria have been developed in the last decade, most prominently the criterion of maximum mutual information. Although this criterion provides for good registration results in many applications, it remains a purely low-level criterion. As a consequence, registration results will deteriorate once this low-level information is corrupted, due to noise, partial occlusions or missing image structure. In this paper, we will develop a Bayesian framework that allows to impose statistically learned prior knowledge about the joint intensity distribution into image registration methods. The prior is given by a kernel density estimate on the space of joint intensity distributions computed from a representative set of pre-registered image pairs. This nonparametric prior accurately models previously learned intensity relations between various image modalities and slice locations. Experimental results demonstrate that the resulting registration process is more robust to missing low-level information as it favors intensity correspondences statistically consistent with the learned intensity distributions.

prior knowledge

low-level information

noise

partial occlusions

missing image structure

statistically learned prior knowledge

joint intensity distribution

joint intensity distributions

intensity relations

image modalities

slice locations

missing low-level information

it

intensity correspondences

intensity distributions

low-level driven image processing algorithms

Bayesian framework

image registration methods

kernel density estimate

registration process

non-rigid image registration

registration

registration criteria

maximum mutual information

low-level criterion

criterion

it

prior

pre-registered image pairs

maximum mutual information

registration criteria

low-level criterion

it

joint intensity distribution

statistically learned prior knowledge

Bayesian framework

image registration methods

statistically learned prior knowledge

image registration methods

kernel density estimate

prior

kernel density estimate

joint intensity distributions

pre-registered image pairs

joint intensity distributions

registration process

missing low-level information

it

intensity correspondences


Input: There is an increased interest in the efficient creation of city models, be it virtual or as-built. We present a method for synthesizing complex, photo-realistic facade images, from a single example. After parsing the example image into its semantic components, a tiling for it is generated. Novel tilings can then be created, yielding facade textures with different dimensions or with occluded parts inpainted. A genetic algorithm guides the novel facades as well as inpainted parts to be consistent with the example, both in terms of their overall structure and their detailed textures. Promising results for multiple standard datasets -- in particular for the different building styles they contain -- demonstrate the potential of the method.

creation of city models

synthesizing complex, photo-realistic facade images

method

genetic algorithm

multiple standard datasets

method

semantic components

tiling

tilings

facade textures

occluded parts inpainted

facades

inpainted parts

method

synthesizing complex, photo-realistic facade images

tiling

semantic components

genetic algorithm

facades

genetic algorithm

inpainted parts

occluded parts inpainted

facade textures

multiple standard datasets

method


Input: We introduce a new interactive corpus exploration tool called InfoMagnets. InfoMagnets aims at making exploratory corpus analysis accessible to researchers who are not experts in text mining. As evidence of its usefulness and usability, it has been used successfully in a research context to uncover relationships between language and behavioral patterns in two distinct domains: tutorial dialogue -LRB- Kumar et al., submitted -RRB- and on-line communities -LRB- Arguello et al., 2006 -RRB-. As an educational tool, it has been used as part of a unit on protocol analysis in an Educational Research Methods course.

interactive corpus exploration tool

InfoMagnets

InfoMagnets

educational tool

exploratory corpus analysis

text mining

protocol analysis

it

domains

it

tutorial dialogue

on-line communities

InfoMagnets

interactive corpus exploration tool

tutorial dialogue

domains

on-line communities

domains

InfoMagnets

exploratory corpus analysis

it

domains

educational tool

protocol analysis

tutorial dialogue

on-line communities


Input: Sources of training data suitable for language modeling of conversational speech are limited. In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams.

language modeling

class-dependent interpolation of N-grams

conversational speech

recognition task

conversational speech

language modeling

class-dependent interpolation of N-grams

recognition task


Input: We present a method for detecting 3D objects using multi-modalities. While it is generic, we demonstrate it on the combination of an image and a dense depth map which give complementary object information. It works in real-time, under heavy clutter, does not require a time consuming training stage, and can handle untextured objects. It is based on an efficient representation of templates that capture the different modalities, and we show in many experiments on commodity hardware that our approach significantly outperforms state-of-the-art methods on single modalities.

method

it

it

It

It

approach

state-of-the-art methods

detecting 3D objects

multi-modalities

image

dense depth map

complementary object information

time consuming training stage

untextured objects

templates

modalities

commodity hardware

single modalities

method

detecting 3D objects

multi-modalities

method

it

it

image

it

dense depth map

it

templates

modalities

approach

single modalities

state-of-the-art methods

single modalities

image

dense depth map

complementary object information

dense depth map

approach

state-of-the-art methods


Input: The compact description of a video sequence through a single image map and a dominant motion has applications in several domains, including video browsing and retrieval, compression, mosaicing, and visual summarization. Building such a representation requires the capability to register all the frames with respect to the dominant object in the scene, a task which has been, in the past, addressed through temporally localized motion estimates. In this paper, we show how the lack of temporal consistency associated with such estimates can undermine the validity of the dominant motion assumption, leading to oscillation between different scene interpretations and poor registration. To avoid this oscillation, we augment the motion model with a generic temporal constraint which increases the robustness against competing interpretations, leading to more meaningful content summarization.

compact description of a video sequence

video browsing and retrieval

compression

mosaicing

visual summarization

content summarization

image map

dominant motion

lack of temporal consistency

temporal consistency

validity of the dominant motion assumption

dominant motion assumption

oscillation between different scene interpretations and poor registration

generic temporal constraint

domains

representation

task

estimates

oscillation

localized motion estimates

motion model

robustness

compact description of a video sequence

domains

image map

compact description of a video sequence

dominant motion

compact description of a video sequence

localized motion estimates

task

generic temporal constraint

motion model

generic temporal constraint

content summarization

image map

dominant motion

video browsing and retrieval

compression

compression

mosaicing

mosaicing

visual summarization

video browsing and retrieval

domains

compression

domains

mosaicing

domains

robustness

generic temporal constraint


Input: In cross-domain learning, there is a more challenging problem that the domain divergence involves more than one dominant factors, e.g., different viewpoints, various resolutions and changing illuminations. Fortunately, an intermediate domain could often be found to build a bridge across them to facilitate the learning problem. In this paper, we propose a Coupled Marginalized Denoising Auto-encoders framework to address the cross-domain problem. Specifically, we design two marginalized denoising auto-encoders, one for the target and the other for source as well as the intermediate one. To better couple the two denoising auto-encoders learning, we incorporate a feature mapping, which tends to transfer knowledge between the intermediate domain and the target one. Furthermore, the maximum margin criterion, e.g., intra-class com-pactness and inter-class penalty, on the output layer is imposed to seek more discriminative features across different domains. Extensive experiments on two tasks have demonstrated the superiority of our method over the state-of-the-art methods.

cross-domain learning

learning problem

cross-domain problem

domain divergence

viewpoints

resolutions

illuminations

intermediate domain

intermediate domain

maximum margin criterion

intra-class com-pactness

inter-class penalty

discriminative features

dominant factors

one

other

tasks

method

state-of-the-art methods

Coupled Marginalized Denoising Auto-encoders framework

marginalized denoising auto-encoders

denoising auto-encoders learning

feature mapping

dominant factors

domain divergence

feature mapping

denoising auto-encoders learning

viewpoints

dominant factors

resolutions

dominant factors

one

marginalized denoising auto-encoders

other

marginalized denoising auto-encoders

intra-class com-pactness

maximum margin criterion

inter-class penalty

maximum margin criterion

viewpoints

resolutions

resolutions

illuminations

one

other

intra-class com-pactness

inter-class penalty

intermediate domain

learning problem

Coupled Marginalized Denoising Auto-encoders framework

cross-domain problem

feature mapping

intermediate domain

tasks

method

method

state-of-the-art methods


Input: In this paper, we aim to automatically render aging faces in a personalized way. Basically, a set of age-group specific dictionaries are learned, where the dictionary bases corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups, and a linear combination of these patterns expresses a particular personalized aging process. Moreover, two factors are taken into consideration in the dictionary learning process. First, beyond the aging dictionaries, each subject may have extra personalized facial characteristics, e.g. mole, which are invariant in the aging process. Second, it is challenging or even impossible to collect faces of all age groups for a particular subject, yet much easier and more practical to get face pairs from neighboring age groups. Thus a personality-aware coupled reconstruction loss is utilized to learn the dictionaries based on face pairs from neighboring age groups. Extensive experiments well demonstrate the advantages of our proposed solution over other state-of-the-arts in term of personalized aging progression, as well as the performance gain for cross-age face verification by synthesizing aging faces.

aging faces

dictionary bases

aging process pattern

personalized facial characteristics

mole

personality-aware coupled reconstruction loss

personalized aging progression

age-group specific dictionaries

linear combination

dictionary learning process

aging dictionaries

synthesizing aging faces

patterns

dictionaries

solution

state-of-the-arts

personalized aging process

aging process

cross-age face verification

dictionary bases

age-group specific dictionaries

linear combination

personalized aging process

patterns

linear combination

personality-aware coupled reconstruction loss

dictionaries

solution

personalized aging progression

state-of-the-arts

personalized aging progression

synthesizing aging faces

cross-age face verification

mole

personalized facial characteristics

solution

state-of-the-arts


Input: We propose a draft scheme of the model formalizing the structure of communicative context in dialogue interaction. The relationships between the interacting partners are considered as system of three automata representing the partners of the dialogue and environment.

model

structure of communicative context

dialogue interaction

model

structure of communicative context

dialogue interaction

structure of communicative context


Input: Visitors who browse the web from wireless PDAs, cell phones, and pagers are frequently stymied by web interfaces optimized for desktop PCs. Simply replacing graphics with text and reformatting tables does not solve the problem, because deep link structures can still require minutes to traverse. In this paper we develop an algorithm, MINPATH, that automatically improves wireless web navigation by suggesting useful shortcut links in real time. MINPATH finds shortcuts by using a learned model of web visitor behavior to estimate the savings of shortcut links, and suggests only the few best links. We explore a variety of predictive models, including Na ¨ ıve Bayes mixture models and mixtures of Markov models, and report empirical evidence that MINPATH finds useful shortcuts that save substantial navigational effort.

web interfaces

MINPATH

MINPATH

Na ¨ ıve Bayes mixture models

mixtures of Markov models

MINPATH

desktop PCs

deep link structures

web visitor behavior

algorithm

model

predictive models

wireless web navigation

savings of shortcut links

web interfaces

desktop PCs

algorithm

wireless web navigation

algorithm

wireless web navigation

model

MINPATH

model

web visitor behavior

model

savings of shortcut links

Na ¨ ıve Bayes mixture models

predictive models

mixtures of Markov models

predictive models

Na ¨ ıve Bayes mixture models

mixtures of Markov models


Input: This paper describes a particular approach to parsing that utilizes recent advances in unification-based parsing and in classification-based knowledge representation. As unification-based grammatical frameworks are extended to handle richer descriptions of linguistic information, they begin to share many of the properties that have been developed in KL-ONE-like knowledge representation systems. This commonality suggests that some of the classification-based representation techniques can be applied to unification-based linguistic descriptions. This merging supports the integration of semantic and syntactic information into the same system, simultaneously subject to the same types of processes, in an efficient manner. The result is expected to be more efficient parsing due to the increased organization of knowledge. The use of a KL-ONE style representation for parsing and semantic interpretation was first explored in the PSI-KLONE system -LSB- 2 -RSB-, in which parsing is characterized as an inference process called incremental description refinement.

approach

they

system

parsing

unification-based parsing

classification-based knowledge representation

parsing

semantic interpretation

parsing

unification-based grammatical frameworks

KL-ONE-like knowledge representation systems

classification-based representation techniques

parsing

KL-ONE style representation

PSI-KLONE system

inference process

incremental description refinement

linguistic information

unification-based linguistic descriptions

semantic and syntactic information

approach

parsing

unification-based parsing

approach

classification-based knowledge representation

approach

unification-based grammatical frameworks

linguistic information

KL-ONE-like knowledge representation systems

they

classification-based representation techniques

unification-based linguistic descriptions

semantic and syntactic information

system

KL-ONE style representation

parsing

KL-ONE style representation

semantic interpretation

PSI-KLONE system

KL-ONE style representation

incremental description refinement

parsing

classification-based knowledge representation

unification-based parsing

parsing

semantic interpretation

incremental description refinement

inference process


Input: In this paper we discuss a proposed user knowledge modeling architecture for the ICICLE system, a language tutoring application for deaf learners of written English. The model will represent the language proficiency of the user and is designed to be referenced during both writing analysis and feedback production. We motivate our model design by citing relevant research on second language and cognitive skill acquisition, and briefly discuss preliminary empirical evidence supporting the design. We conclude by showing how our design can provide a rich and robust information base to a language assessment/correction application by modeling user proficiency at a high level of granularity and specificity.

user knowledge modeling architecture

ICICLE system

language tutoring application

writing analysis

feedback production

second language and cognitive skill acquisition

language assessment/correction application

deaf learners

user proficiency

written English

model

model design

design

design

granularity

specificity

user knowledge modeling architecture

ICICLE system

language tutoring application

deaf learners

written English

language tutoring application

model

writing analysis

model

feedback production

second language and cognitive skill acquisition

model design

design

language assessment/correction application

design

user proficiency

ICICLE system

language tutoring application

writing analysis

feedback production

granularity

specificity

granularity

user proficiency

specificity

user proficiency


Input: Constraint propagation is one of the key techniques in constraint programming, and a large body of work has built up around it. Special-purpose constraint propagation algorithms frequently make implicit use of short supports -- by examining a subset of the variables, they can infer support -LRB- a justification that a variable-value pair still forms part of a solution to the constraint -RRB- for all other variables and values and save substantial work. Recently short supports have been used in general purpose prop-agators, and -LRB- when the constraint is amenable to short supports -RRB- speed ups of more than three orders of magnitude have been demonstrated. In this paper we present SHORTSTR2, a development of the Simple Tabular Reduction algorithm STR2 +. We show that SHORTSTR2 is complementary to the existing algorithms SHORTGAC and HAGGISGAC that exploit short supports, while being much simpler. When a constraint is amenable to short supports, the short support set can be exponentially smaller than the full-length support set. Therefore SHORTSTR2 can efficiently propagate many constraints that STR2 + can not even load into memory. We also show that SHORTSTR2 can be combined with a simple algorithm to identify short supports from full-length supports, to provide a superior drop-in replacement for STR2 +.

Constraint propagation

constraint programming

Special-purpose constraint propagation algorithms

general purpose prop-agators

Simple Tabular Reduction algorithm STR2 +

SHORTGAC

HAGGISGAC

STR2 +

STR2 +

constraint

constraint

short supports

short support set

full-length support set

constraints

short supports

full-length supports

drop-in replacement

algorithm

Constraint propagation

constraint programming

Simple Tabular Reduction algorithm STR2 +

short supports

drop-in replacement

algorithm

short supports

full-length supports

full-length supports

full-length supports

algorithm

full-length supports

algorithm

drop-in replacement

STR2 +

SHORTGAC

HAGGISGAC

short support set

full-length support set

SHORTGAC

HAGGISGAC

algorithm


Input: We propose a detection method for orthographic variants caused by transliteration in a large corpus. The method employs two similarities. One is string similarity based on edit distance. The other is contextual similarity by a vector space model. Experimental results show that the method performed a 0.889 F-measure in an open test.

detection method

edit distance

vector space model

orthographic variants

string similarity

contextual similarity

transliteration

method

similarities

method

F-measure

detection method

orthographic variants

similarities

method

edit distance

string similarity

vector space model

contextual similarity

F-measure

method


Input: Uncertainty handling plays an important role during shape tracking. We have recently shown that the fusion of measurement information with system dynamics and shape priors greatly improves the tracking performance for very noisy images such as ultrasound sequences -LSB- 22 -RSB-. Nevertheless, this approach required user initialization of the tracking process. This paper solves the automatic initial-ization problem by performing boosted shape detection as a generic measurement process and integrating it in our tracking framework. We show how to propagate the local detection uncertainties of multiple shape candidates during shape alignment, fusion with the predicted shape prior, and fusion with subspace constraints. As a result, we treat all sources of information in a unified way and derive the posterior shape model as the shape with the maximum likelihood. Our framework is applied for the automatic tracking of endocardium in ultrasound sequences of the human heart. Reliable detection and robust tracking results are achieved when compared to existing approaches and inter-expert variations.

Uncertainty handling

shape tracking

tracking

automatic initial-ization problem

automatic tracking of endocardium

detection

tracking

fusion of measurement information with system dynamics and shape priors

tracking process

boosted shape detection

generic measurement process

tracking framework

posterior shape model

noisy images

ultrasound sequences

ultrasound sequences of the human heart

approach

it

framework

approaches

user initialization

local detection uncertainties

local detection uncertainties of multiple shape candidates

shape alignment

predicted shape prior

subspace constraints

maximum likelihood

endocardium

inter-expert variations

Uncertainty handling

shape tracking

fusion of measurement information with system dynamics and shape priors

tracking

tracking

noisy images

user initialization

approach

user initialization

tracking process

boosted shape detection

automatic initial-ization problem

generic measurement process

boosted shape detection

maximum likelihood

posterior shape model

framework

automatic tracking of endocardium

ultrasound sequences

noisy images

it

tracking framework

endocardium

ultrasound sequences of the human heart

detection

tracking

approaches

inter-expert variations


Input: We present a syntax-based constraint for word alignment, known as the cohesion constraint. It requires disjoint English phrases to be mapped to non-overlapping intervals in the French sentence. We evaluate the utility of this constraint in two different algorithms. The results show that it can provide a significant improvement in alignment quality.

syntax-based constraint

cohesion constraint

word alignment

It

constraint

algorithms

it

English phrases

French sentence

alignment quality

syntax-based constraint

word alignment

English phrases

It

cohesion constraint

syntax-based constraint

algorithms

constraint

alignment quality

it


Input: This paper considers the problem of automatic assessment of local coherence. We present a novel entity-based representation of discourse which is inspired by Centering Theory and can be computed automatically from raw text. We view coherence assessment as a ranking learning problem and show that the proposed discourse representation supports the effective learning of a ranking function. Our experiments demonstrate that the induced model achieves significantly higher accuracy than a state-of-the-art coherence model.

automatic assessment of local coherence

coherence assessment

ranking learning problem

entity-based representation of discourse

Centering Theory

discourse representation

induced model

coherence model

raw text

ranking function

accuracy

Centering Theory

entity-based representation of discourse

raw text

entity-based representation of discourse

ranking learning problem

coherence assessment

discourse representation

ranking function

induced model

coherence model

accuracy

induced model

accuracy

coherence model


Input: This paper introduces a robust interactive method for speech understanding. The generalized LR parsing is enhanced in this approach. Parsing proceeds from left to right correcting minor errors. When a very noisy portion is detected, the parser skips that portion using a fake non-terminal symbol. The unidentified portion is resolved by re-utterance of that portion which is parsed very efficiently by using the parse record of the first utterance. The user does not have to speak the whole sentence again. This method is also capable of handling unknown words, which is important in practical systems. Detected unknown words can be incrementally incorporated into the dictionary after the interaction with the user. A pilot system has shown great effectiveness of this approach.

robust interactive method

approach

method

approach

speech understanding

generalized LR parsing

Parsing

parser

non-terminal symbol

unknown words

robust interactive method

speech understanding

approach

generalized LR parsing

non-terminal symbol

parser

method

unknown words


Input: This paper shows that it is very often possible to identify the source language of medium-length speeches in the EUROPARL corpus on the basis of frequency counts of word n-grams -LRB- 87.2% -96.7% accuracy depending on classification method -RRB-. The paper also examines in detail which positive markers are most powerful and identifies a number of linguistic aspects as well as culture-and domain-related ones.

medium-length speeches

EUROPARL corpus

frequency counts of word n-grams

classification method

word n-grams

positive markers

accuracy

medium-length speeches

EUROPARL corpus

accuracy

classification method


Input: We investigated whether automatic phonetic transcriptions -LRB- APTs -RRB- can replace manually verified phonetic transcriptions -LRB- MPTs -RRB- in a large corpus-based study on pronunciation variation. To this end, we compared the performance of both transcription types in a classification experiment aimed at establishing the direct influence of a particular situational setting on pronunciation variation. We trained classifiers on the speech processes extracted from the alignments of an APT and an MPT with a canonical transcription. We tested whether the classifiers were equally good at verifying whether unknown transcriptions represent read speech or telephone dialogues, and whether the same speech processes were identified to distinguish between transcriptions of the two situational settings. Our results not only show that similar distinguishing speech processes were identified; our APT-based classifier yielded better classification accuracy than the MPT-based classifier whilst using fewer classification features.

automatic phonetic transcriptions -LRB- APTs -RRB-

manually verified phonetic transcriptions

alignments

APT

MPT

canonical transcription

unknown transcriptions

classification features

pronunciation variation

classification

pronunciation variation

classifiers

APT-based classifier

MPT-based classifier

speech processes

classifiers

read speech

telephone dialogues

speech processes

classification accuracy

automatic phonetic transcriptions -LRB- APTs -RRB-

manually verified phonetic transcriptions

APT-based classifier

MPT-based classifier

automatic phonetic transcriptions -LRB- APTs -RRB-

pronunciation variation

manually verified phonetic transcriptions

pronunciation variation

speech processes

classifiers

alignments

speech processes

alignments

APT

alignments

MPT

canonical transcription

alignments

classifiers

unknown transcriptions

unknown transcriptions

read speech

unknown transcriptions

telephone dialogues

classification features

APT-based classifier

classification features

MPT-based classifier

APT

MPT

read speech

telephone dialogues

classification accuracy

APT-based classifier

classification accuracy

MPT-based classifier


Input: Machine reading is a relatively new field that features computer programs designed to read flowing text and extract fact assertions expressed by the narrative content. This task involves two core technologies: natural language processing -LRB- NLP -RRB- and information extraction -LRB- IE -RRB-. In this paper we describe a machine reading system that we have developed within a cognitive architecture. We show how we have integrated into the framework several levels of knowledge for a particular domain, ideas from cognitive semantics and construction grammar, plus tools from prior NLP and IE research. The result is a system that is capable of reading and interpreting complex and fairly idiosyncratic texts in the family history domain. We describe the architecture and performance of the system. After presenting the results from several evaluations that we have carried out, we summarize possible future directions.

Machine reading

information extraction -LRB- IE -RRB-

machine reading system

prior NLP

IE research

field

task

system

system

computer programs

natural language processing -LRB- NLP -RRB-

cognitive architecture

construction grammar

flowing text

narrative content

idiosyncratic texts

family history domain

fact assertions

cognitive semantics

computer programs

flowing text

computer programs

fact assertions

system

idiosyncratic texts

fact assertions

narrative content

cognitive architecture

machine reading system

family history domain

idiosyncratic texts

natural language processing -LRB- NLP -RRB-

task

information extraction -LRB- IE -RRB-

task

cognitive semantics

construction grammar

prior NLP

IE research


Input: Nonstationary chaotic behavior is not an oxymoron. We present two methods for capturing nonstationary chaos, then present a few examples including biological signals, ocean waves and traffic flow. The issue is of practical interest because it is often useful to capture when nonstationary events take place and it is desirable to know over what periods a signal is stationary.

Nonstationary chaotic behavior

oxymoron

nonstationary chaos

nonstationary events

methods

examples

biological signals

ocean waves

traffic flow

methods

nonstationary chaos

biological signals

examples

ocean waves

examples

traffic flow

examples

biological signals

ocean waves

ocean waves

traffic flow


Input: This paper presents a formal analysis for a large class of words called alternative markers, which includes other -LRB- than -RRB-, such -LRB- as -RRB-, and besides. These words appear frequently enough in dialog to warrant serious attention, yet present natural language search engines perform poorly on queries containing them. I show that the performance of a search engine can be improved dramatically by incorporating an approximation of the formal analysis that is compatible with the search engine's operational semantics. The value of this approach is that as the operational semantics of natural language applications improve, even larger improvements are possible.

formal analysis

natural language search engines

search engine

approximation of the formal analysis

formal analysis

search engine

alternative markers

dialog

operational semantics

operational semantics

words

them

approach

natural language applications

formal analysis

alternative markers

words

dialog

approximation of the formal analysis

search engine

operational semantics

search engine

operational semantics

natural language applications


Input: In this paper, we address the problem of combining several language models -LRB- LMs -RRB-. We find that simple interpolation methods, like log-linear and linear interpolation, improve the performance but fall short of the performance of an oracle. The oracle knows the reference word string and selects the word string with the best performance -LRB- typically, word or semantic error rate -RRB- from a list of word strings, where each word string has been obtained by using a different LM. Actually, the oracle acts like a dynamic combiner with hard decisions using the reference. We provide experimental results that clearly show the need for a dynamic language model combination to improve the performance further. We suggest a method that mimics the behavior of the oracle using a neural network or a decision tree. The method amounts to tagging LMs with confidence measures and picking the best hypothesis corresponding to the LM with the best confidence.

language models -LRB- LMs -RRB-

interpolation methods

log-linear and linear interpolation

LM

dynamic combiner

dynamic language model combination

neural network

decision tree

LMs

LM

word or semantic error rate

confidence measures

hard decisions

method

method

log-linear and linear interpolation

interpolation methods

hard decisions

dynamic combiner

neural network

method

decision tree

method

method

LMs

confidence measures

method

decision tree

neural network


Input: We describe a new method for the representation of NLP structures within reranking approaches. We make use of a conditional log-linear model, with hidden variables representing the assignment of lexical items to word clusters or word senses. The model learns to automatically make these assignments based on a discriminative training criterion. Training and decoding with the model requires summing over an exponential number of hidden-variable assignments: the required summations can be computed efficiently and exactly using dynamic programming. As a case study, we apply the model to parse reranking. The model gives an F-measure improvement of ~ 1.25% beyond the base parser, and an ~ 0.25% improvement beyond Collins -LRB- 2000 -RRB- reranker. Although our experiments are focused on parsing, the techniques described generalize naturally to NLP structures other than parse trees.

method

model

model

summations

model

model

techniques

NLP structures

hidden variables

word clusters

word senses

hidden-variable assignments

NLP structures

parse trees

reranking approaches

conditional log-linear model

dynamic programming

base parser

Collins -LRB- 2000 -RRB- reranker

discriminative training criterion

F-measure

parse reranking

parsing

method

NLP structures

hidden variables

conditional log-linear model

discriminative training criterion

model

dynamic programming

summations

model

parse reranking

techniques

parsing

techniques

NLP structures

techniques

parse trees

reranking approaches

NLP structures

word clusters

word senses

parse trees

NLP structures

model

base parser

base parser

Collins -LRB- 2000 -RRB- reranker

F-measure

model


Input: This paper presents an algorithm for learning the time-varying shape of a non-rigid 3D object from uncalibrated 2D tracking data. We model shape motion as a rigid component -LRB- rotation and translation -RRB- combined with a non-rigid deformation. Reconstruction is ill-posed if arbitrary deformations are allowed. We constrain the problem by assuming that the object shape at each time instant is drawn from a Gaussian distribution. Based on this assumption, the algorithm simultaneously estimates 3D shape and motion for each time frame, learns the parameters of the Gaussian, and robustly fills-in missing data points. We then extend the algorithm to model temporal smoothness in object shape, thus allowing it to handle severe cases of missing data.

algorithm

algorithm

algorithm

it

learning the time-varying shape of a non-rigid 3D object

uncalibrated 2D tracking data

missing data

shape motion

rotation

translation

non-rigid deformation

Reconstruction

arbitrary deformations

object shape

3D shape and motion

temporal smoothness in object shape

rigid component

Gaussian distribution

Gaussian

algorithm

learning the time-varying shape of a non-rigid 3D object

Gaussian distribution

object shape

algorithm

3D shape and motion

algorithm

temporal smoothness in object shape

it

missing data


Input: Automatic summarization and information extraction are two important Internet services. MUC and SUMMAC play their appropriate roles in the next generation Internet. This paper focuses on the automatic summarization and proposes two different models to extract sentences for summary generation under two tasks initiated by SUMMAC-1. For categorization task, positive feature vectors and negative feature vectors are used cooperatively to construct generic, indicative summaries. For adhoc task, a text model based on relationship between nouns and verbs is used to filter out irrelevant discourse segment, to rank relevant sentences, and to generate the user-directed summaries. The result shows that the NormF of the best summary and that of the fixed summary for adhoc tasks are 0.456 and 0. 447. The NormF of the best summary and that of the fixed summary for categorization task are 0.4090 and 0.4023. Our system outperforms the average system in categorization task but does a common job in adhoc task.

Automatic summarization

information extraction

automatic summarization

summary generation

categorization task

adhoc task

adhoc tasks

categorization task

categorization task

adhoc task

MUC

SUMMAC

models

tasks

system

system

positive feature vectors

negative feature vectors

generic, indicative summaries

discourse segment

user-directed summaries

text model

NormF

NormF

Automatic summarization

information extraction

MUC

SUMMAC

positive feature vectors

negative feature vectors

models

summary generation

models

tasks

positive feature vectors

categorization task

positive feature vectors

generic, indicative summaries

negative feature vectors

categorization task

negative feature vectors

generic, indicative summaries

text model

adhoc task

text model

discourse segment

text model

user-directed summaries

tasks

NormF

adhoc tasks

NormF

categorization task

categorization task

system

categorization task

system

adhoc task

system

adhoc task

categorization task

system

system


Input: In real-world action recognition problems, low-level features can not adequately characterize the rich spatial-temporal structures in action videos. In this work, we encode actions based on attributes that describes actions as high-level concepts e.g., jump forward or motion in the air. We base our analysis on two types of action attributes. One type of action attributes is generated by humans. The second type is data-driven attributes, which are learned from data using dictionary learning methods. Attribute-based representation may exhibit high variance due to noisy and redundant attributes. We propose a discriminative and compact attribute-based representation by selecting a subset of discriminative attributes from a large attribute set. Three attribute selection criteria are proposed and formulated as a submodular optimization problem. A greedy optimization algorithm is presented and guaranteed to be at least -LRB- 1-1/e -RRB--approximation to the optimum. Experimental results on the Olympic Sports and UCF101 datasets demonstrate that the proposed attribute-based representation can significantly boost the performance of action recognition algorithms and outperform most recently proposed recognition approaches.

real-world action recognition problems

submodular optimization problem

low-level features

rich spatial-temporal structures

high-level concepts

action attributes

action attributes

data-driven attributes

noisy and redundant attributes

discriminative attributes

action videos

Olympic Sports and UCF101 datasets

dictionary learning methods

Attribute-based representation

discriminative and compact attribute-based representation

greedy optimization algorithm

attribute-based representation

action recognition algorithms

recognition approaches

attribute selection criteria

rich spatial-temporal structures

action videos

dictionary learning methods

data-driven attributes

discriminative attributes

discriminative and compact attribute-based representation

submodular optimization problem

attribute selection criteria

attribute-based representation

action recognition algorithms

Olympic Sports and UCF101 datasets

attribute-based representation

action recognition algorithms

recognition approaches


Input: This paper reports a completed stage of ongoing research at the University of York. Landsbergen's advocacy of analytical inverses for compositional syntax rules encourages the application of Definite Clause Grammar techniques to the construction of a parser returning Montague analysis trees. A parser MDCC is presented which implements an augmented Friedman-Warren algorithm permitting post referencing * and interfaces with a language of intenslonal logic translator LILT so as to display the derivational history of corresponding reduced IL formulae. Some familiarity with Montague's PTQ and the basic DCG mechanism is assumed.

analytical inverses

Definite Clause Grammar techniques

parser MDCC

augmented Friedman-Warren algorithm

Montague's PTQ

basic DCG mechanism

compositional syntax rules

post referencing

intenslonal logic translator LILT

derivational history

reduced IL formulae

parser returning Montague analysis trees

analytical inverses

compositional syntax rules

analytical inverses

Definite Clause Grammar techniques

Definite Clause Grammar techniques

parser returning Montague analysis trees

augmented Friedman-Warren algorithm

parser MDCC

derivational history

intenslonal logic translator LILT

post referencing

augmented Friedman-Warren algorithm

reduced IL formulae

derivational history

Montague's PTQ

basic DCG mechanism


Input: Despite their success, convolutional neural networks are computationally expensive because they must examine all image locations. Stochastic attention-based models have been shown to improve computational efficiency at test time, but they remain difficult to train because of intractable posterior inference and high variance in the stochastic gradient estimates. Borrowing techniques from the literature on training deep generative models, we present the Wake-Sleep Recurrent Attention Model, a method for training stochastic attention networks which improves posterior inference and which reduces the variability in the stochastic gradients. We show that our method can greatly speed up the training time for stochastic attention networks in the domains of image classification and caption generation.

convolutional neural networks

Stochastic attention-based models

Borrowing techniques

deep generative models

Wake-Sleep Recurrent Attention Model

stochastic attention networks

stochastic attention networks

they

they

method

method

computational efficiency

training time

intractable posterior inference

stochastic gradient estimates

posterior inference

image classification

caption generation

stochastic gradients

computational efficiency

Stochastic attention-based models

training time

method

image classification

method

caption generation

method

intractable posterior inference

stochastic gradient estimates

image classification

caption generation

Borrowing techniques

deep generative models

method

stochastic attention networks

stochastic attention networks

posterior inference

training time

stochastic attention networks


Input: A new exemplar-based framework unifying image completion, texture synthesis and image inpainting is presented in this work. Contrary to existing greedy techniques, these tasks are posed in the form of a discrete global optimization problem with a well defined objective function. For solving this problem a novel optimization scheme, called Priority-BP, is proposed which carries two very important extensions over standard belief propagation -LRB- BP -RRB-:'' priority-based message scheduling'' and'' dynamic label pruning''. These two extensions work in cooperation to deal with the intolerable computational cost of BP caused by the huge number of existing labels. Moreover, both extensions are generic and can therefore be applied to any MRF energy function as well. The effectiveness of our method is demonstrated on a wide variety of image completion examples.

exemplar-based framework

greedy techniques

optimization scheme

Priority-BP

belief propagation -LRB- BP -RRB-

priority-based message scheduling

dynamic label pruning

BP

MRF energy function

image completion

texture synthesis

image inpainting

discrete global optimization problem

tasks

problem

extensions

extensions

extensions

method

well defined objective function

intolerable computational cost of BP

image completion examples

exemplar-based framework

image completion

exemplar-based framework

texture synthesis

exemplar-based framework

image inpainting

optimization scheme

problem

belief propagation -LRB- BP -RRB-

extensions

extensions

intolerable computational cost of BP

extensions

MRF energy function

image completion examples

method

image completion

texture synthesis

texture synthesis

image inpainting

priority-based message scheduling

dynamic label pruning

greedy techniques

tasks

discrete global optimization problem

tasks

well defined objective function

discrete global optimization problem

Priority-BP

optimization scheme

priority-based message scheduling

extensions

dynamic label pruning

extensions

extensions

optimization scheme


Input: In this paper, we compare the relative effects of segment order, segmentation and segment contiguity on the retrieval performance of a translation memory system. We take a selection of both bag-of-words and segment order-sensitive string comparison methods, and run each over both character-and word-segmented data, in combination with a range of local segment contiguity models -LRB- in the form of N-grams -RRB-. Over two distinct datasets, we find that indexing according to simple character bigrams produces a retrieval accuracy superior to any of the tested word N-gram models. Further, in their optimum configuration, bag-of-words methods are shown to be equivalent to segment order-sensitive methods in terms of retrieval accuracy, but much faster. We also provide evidence that our findings are scalable.

segment order

segmentation

segment contiguity

retrieval

indexing

translation memory system

bag-of-words and segment order-sensitive string comparison methods

local segment contiguity models

N-grams

character bigrams

word N-gram models

bag-of-words methods

segment order-sensitive methods

character-and word-segmented data

retrieval accuracy

retrieval accuracy

segment order

segmentation

segmentation

segment contiguity

local segment contiguity models

bag-of-words and segment order-sensitive string comparison methods

segment order

translation memory system

segmentation

translation memory system

segment contiguity

translation memory system

character-and word-segmented data

bag-of-words and segment order-sensitive string comparison methods

character bigrams

indexing

retrieval

translation memory system

retrieval accuracy

character bigrams

retrieval accuracy

word N-gram models

retrieval accuracy

bag-of-words methods

retrieval accuracy

segment order-sensitive methods

N-grams

local segment contiguity models

character bigrams

word N-gram models

bag-of-words methods

segment order-sensitive methods


Input: In this paper we show how two standard outputs from information extraction -LRB- IE -RRB- systems-named entity annotations and scenario templates-can be used to enhance access to text collections via a standard text browser. We describe how this information is used in a prototype system designed to support information workers' access to a pharmaceutical news archive as part of their industry watch function. We also report results of a preliminary, qualitative user evaluation of the system, which while broadly positive indicates further work needs to be done on the interface to make users aware of the increased potential of IE-enhanced text browsers.

outputs

prototype system

system

information extraction -LRB- IE -RRB- systems

text browser

IE-enhanced text browsers

named entity annotations

scenario templates

text collections

pharmaceutical news archive

qualitative user evaluation

outputs

text collections

text browser

text collections

prototype system

pharmaceutical news archive

named entity annotations

outputs

scenario templates

outputs

named entity annotations

scenario templates

qualitative user evaluation

system


Input: We present a new model-based bundle adjustment algorithm to recover the 3D model of a scene/object from a sequence of images with unknown motions. Instead of representing scene/object by a collection of isolated 3D features -LRB- usually points -RRB-, our algorithm uses a surface controlled by a small set of parameters. Compared with previous model-based approaches, our approach has the following advantages. First, instead of using the model space as a regular-izer, we directly use it as our search space, thus resulting in a more elegant formulation with fewer unknowns and fewer equations. Second, our algorithm automatically associates tracked points with their correct locations on the surfaces, thereby eliminating the need for a prior 2D-to-3D association. Third, regarding face modeling, we use a very small set of face metrics -LRB- meaningful deformations -RRB- to parame-terize the face geometry, resulting in a smaller search space and a better posed system. Experiments with both synthetic and real data show that this new algorithm is faster, more accurate and more stable than existing ones.

model-based bundle adjustment algorithm

3D model

posed system

images

synthetic and real data

unknown motions

isolated 3D features

surface

model space

regular-izer

search space

surfaces

prior 2D-to-3D association

face geometry

search space

algorithm

model-based approaches

approach

it

algorithm

algorithm

ones

face modeling

face metrics

model-based bundle adjustment algorithm

3D model

images

3D model

surface

algorithm

model space

regular-izer

it

search space

face metrics

face modeling

face metrics

face geometry

face metrics

search space

face metrics

posed system

unknown motions

images

model-based approaches

approach

regular-izer

search space

algorithm

ones

synthetic and real data

algorithm

synthetic and real data

ones


Input: This paper presents an approach to the unsupervised learning of parts of speech which uses both morphological and syntactic information. While the model is more complex than those which have been employed for unsupervised learning of POS tags in English, which use only syntactic information, the variety of languages in the world requires that we consider morphology as well. In many languages, morphology provides better clues to a word's category than word order. We present the computational model for POS learning, and present results for applying it to Bulgarian, a Slavic language with relatively free word order and rich morphology.

approach

model

those

computational model

it

unsupervised learning of parts of speech

unsupervised learning of POS tags in English

POS learning

morphological and syntactic information

syntactic information

morphology

morphology

word order

free word order

rich morphology

Bulgarian

Slavic language

approach

unsupervised learning of parts of speech

morphological and syntactic information

approach

those

unsupervised learning of POS tags in English

syntactic information

those

computational model

POS learning

Bulgarian

it

model

those

morphology

word order

Bulgarian

Slavic language

free word order

Bulgarian

rich morphology

Bulgarian

free word order

rich morphology


Input: Words in Chinese text are not naturally separated by delimiters, which poses a challenge to standard machine translation -LRB- MT -RRB- systems. In MT, the widely used approach is to apply a Chinese word segmenter trained from manually annotated data, using a fixed lexicon. Such word segmentation is not necessarily optimal for translation. We propose a Bayesian semi-supervised Chinese word segmentation model which uses both monolingual and bilingual information to derive a segmentation suitable for MT. Experiments show that our method improves a state-of-the-art MT system in a small and a large data environment.

Chinese text

manually annotated data

machine translation -LRB- MT -RRB- systems

MT

translation

segmentation

MT

Chinese word segmenter

word segmentation

Bayesian semi-supervised Chinese word segmentation model

MT system

monolingual and bilingual information

method

Chinese word segmenter

MT

manually annotated data

Chinese word segmenter

word segmentation

translation

Bayesian semi-supervised Chinese word segmentation model

segmentation

monolingual and bilingual information

Bayesian semi-supervised Chinese word segmentation model

segmentation

MT

method

MT system


Input: In this paper we compare two competing approaches to part-of-speech tagging, statistical and constraint-based disambiguation, using French as our test language. We imposed a time limit on our experiment: the amount of time spent on the design of our constraint system was about the same as the time we used to train and test the easy-to-implement statistical model. We describe the two systems and compare the results. The accuracy of the statistical method is reasonably good, comparable to taggers for English. But the constraint-based tagger seems to be superior even with the limited time we allowed ourselves for rule development.

approaches

constraint system

systems

part-of-speech tagging

statistical and constraint-based disambiguation

French

English

statistical model

statistical method

taggers

constraint-based tagger

accuracy

approaches

part-of-speech tagging

French

approaches

taggers

English

constraint system

statistical model

statistical method

taggers

accuracy

statistical method

accuracy

taggers


Input: Structured-light methods actively generate geometric correspondence data between projectors and cameras in order to facilitate robust 3D reconstruction. In this paper, we present Photogeometric Structured Light whereby a standard structured light method is extended to include photometric methods. Photometric processing serves the double purpose of increasing the amount of recovered surface detail and of enabling the structured-light setup to be robustly self-calibrated. Further, our framework uses a photogeometric optimization that supports the simultaneous use of multiple cameras and projectors and yields a single and accurate multi-view 3D model which best complies with photometric and geometric data.

Structured-light methods

Photogeometric Structured Light

structured light method

photometric methods

Photometric processing

photogeometric optimization

multi-view 3D model

geometric correspondence data

photometric and geometric data

robust 3D reconstruction

recovered surface detail

structured-light setup

framework

Structured-light methods

geometric correspondence data

geometric correspondence data

robust 3D reconstruction

Photometric processing

recovered surface detail

Photometric processing

structured-light setup

photogeometric optimization

framework

photometric and geometric data

multi-view 3D model

structured light method

Photogeometric Structured Light

photometric methods

Photogeometric Structured Light


Input: In this paper, a discrimination and robustness oriented adaptive learning procedure is proposed to deal with the task of syntactic ambiguity resolution. Owing to the problem of insufficient training data and approximation error introduced by the language model, traditional statistical approaches, which resolve ambiguities by indirectly and implicitly using maximum likelihood method, fail to achieve high performance in real applications. The proposed method remedies these problems by adjusting the parameters to maximize the accuracy rate directly. To make the proposed algorithm robust, the possible variations between the training corpus and the real tasks are also taken into consideration by enlarging the separation margin between the correct candidate and its competing members. Significant improvement has been observed in the test. The accuracy rate of syntactic disambiguation is raised from 46.0% to 60.62% by using this novel approach.

adaptive learning procedure

language model

statistical approaches

maximum likelihood method

syntactic ambiguity resolution

syntactic disambiguation

insufficient training data

approximation error

ambiguities

separation margin

method

problems

algorithm

approach

accuracy rate

adaptive learning procedure

syntactic ambiguity resolution

statistical approaches

ambiguities

maximum likelihood method

statistical approaches

insufficient training data

approximation error

accuracy rate

syntactic disambiguation

syntactic disambiguation

approach


Input: This paper presents a new approach to statistical sentence generation in which alternative phrases are represented as packed sets of trees, or forests, and then ranked statistically to choose the best one. This representation offers advantages in compactness and in the ability to represent syntactic information. It also facilitates more efficient statistical ranking than a previous approach to statistical generation. An efficient ranking algorithm is described, together with experimental results showing significant improvements over simple enumeration or a lattice-based approach.

approach

representation

It

approach

statistical sentence generation

statistical generation

trees

forests

syntactic information

statistical ranking

ranking algorithm

enumeration

lattice-based approach

approach

statistical sentence generation

It

statistical ranking

approach

statistical generation

It

approach

ranking algorithm

enumeration

ranking algorithm

lattice-based approach

enumeration

lattice-based approach


Input: This article deals with the interpretation of conceptual operations underlying the communicative use of natural language -LRB- NL -RRB- within the Structured Inheritance Network -LRB- SI-Nets -RRB- paradigm. The operations are reduced to functions of a formal language, thus changing the level of abstraction of the operations to be performed on SI-Nets. In this sense, operations on SI-Nets are not merely isomorphic to single epistemological objects, but can be viewed as a simulation of processes on a different level, that pertaining to the conceptual system of NL. For this purpose, we have designed a version of KL-ONE which represents the epistemological level, while the new experimental language, KL-Conc, represents the conceptual level. KL-Conc would seem to be a more natural and intuitive way of interacting with SI-Nets.

conceptual operations

natural language -LRB- NL -RRB-

NL

Structured Inheritance Network -LRB- SI-Nets -RRB- paradigm

SI-Nets

SI-Nets

conceptual system

KL-ONE

KL-Conc

KL-Conc

SI-Nets

operations

operations

operations

experimental language

formal language

epistemological objects

epistemological level

conceptual level

natural language -LRB- NL -RRB-

Structured Inheritance Network -LRB- SI-Nets -RRB- paradigm

operations

SI-Nets

operations

SI-Nets

NL

conceptual system

KL-ONE

KL-Conc

epistemological level

KL-ONE

conceptual level

KL-Conc


Input: We present an algorithm for calibrated camera relative pose estimation from lines. Given three lines with two of the lines parallel and orthogonal to the third we can compute the relative rotation between two images. We can also compute the relative translation from two intersection points. We also present a framework in which such lines can be detected. We evaluate the performance of the algorithm using synthetic and real data. The intended use of the algorithm is with robust hypothesize-and-test frameworks such as RANSAC. Our approach is suitable for urban and indoor environments where most lines are either parallel or orthogonal to each other.

algorithm

framework

algorithm

algorithm

approach

calibrated camera relative pose estimation

relative rotation

relative translation

urban and indoor environments

synthetic and real data

hypothesize-and-test frameworks

RANSAC

algorithm

calibrated camera relative pose estimation

synthetic and real data

algorithm

approach

urban and indoor environments

algorithm

hypothesize-and-test frameworks

RANSAC

hypothesize-and-test frameworks


Input: In this paper, we present a fully automated extraction system, named IntEx, to identify gene and protein interactions in biomedical text. Our approach is based on first splitting complex sentences into simple clausal structures made up of syntactic roles. Then, tagging biological entities with the help of biomedical and linguistic ontologies. Finally, extracting complete interactions by analyzing the matching contents of syntactic roles and their linguistically significant combinations. Our extraction system handles complex sentences and extracts multiple and nested interactions specified in a sentence. Experimental evaluations with two other state of the art extraction systems indicate that the IntEx system achieves better performance without the labor intensive pattern engineering requirement.

fully automated extraction system

IntEx

extraction system

extraction systems

IntEx system

gene and protein interactions

biomedical text

approach

syntactic roles

biological entities

biomedical and linguistic ontologies

syntactic roles

multiple and nested interactions

pattern engineering requirement

fully automated extraction system

gene and protein interactions

biomedical text

gene and protein interactions

biomedical and linguistic ontologies

biological entities

extraction system

multiple and nested interactions

IntEx

fully automated extraction system

IntEx system

extraction systems


Input: This paper introduces a method for computational analysis of move structures in abstracts of research articles. In our approach, sentences in a given abstract are analyzed and labeled with a specific move in light of various rhetorical functions. The method involves automatically gathering a large number of abstracts from the Web and building a language model of abstract moves. We also present a prototype concordancer, CARE, which exploits the move-tagged abstracts for digital learning. This system provides a promising approach to Web-based computer-assisted academic writing.

method

approach

method

system

approach

computational analysis of move structures

digital learning

Web-based computer-assisted academic writing

abstracts of research articles

abstracts

Web

abstract moves

move-tagged abstracts

rhetorical functions

language model

prototype concordancer

CARE

method

computational analysis of move structures

abstracts of research articles

computational analysis of move structures

Web

abstracts

abstract moves

language model

CARE

move-tagged abstracts

move-tagged abstracts

digital learning

system

approach

approach

Web-based computer-assisted academic writing

CARE

prototype concordancer


Input: This work presents a real-time system for multiple object tracking in dynamic scenes. A unique characteristic of the system is its ability to cope with long-duration and complete occlusion without a prior knowledge about the shape or motion of objects. The system produces good segment and tracking results at a frame rate of 15-20 fps for image size of 320x240, as demonstrated by extensive experiments performed using video sequences under different conditions indoor and outdoor with long-duration and complete occlusions in changing background.

real-time system

multiple object tracking in dynamic scenes

tracking

system

system

long-duration and complete occlusion

prior knowledge

shape

motion of objects

frame rate

image size

long-duration and complete occlusions

changing background

video sequences

real-time system

multiple object tracking in dynamic scenes

system

long-duration and complete occlusion

prior knowledge

shape

prior knowledge

motion of objects

shape

motion of objects

tracking

system


Input: We propose a method of organizing reading materials for vocabulary learning. It enables us to select a concise set of reading texts -LRB- from a target corpus -RRB- that contains all the target vocabulary to be learned. We used a specialized vocabulary for an English certification test as the target vocabulary and used English Wikipedia, a free-content encyclopedia, as the target corpus. The organized reading materials would enable learners not only to study the target vocabulary efficiently but also to gain a variety of knowledge through reading. The reading materials are available on our web site.

method

It

organizing reading materials

vocabulary learning

target vocabulary

English Wikipedia

free-content encyclopedia

method

organizing reading materials

organizing reading materials

vocabulary learning

English Wikipedia

free-content encyclopedia


Input: A novel bootstrapping approach to Named Entity -LRB- NE -RRB- tagging using concept-based seeds and successive learners is presented. This approach only requires a few common noun or pronoun seeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE. The bootstrapping procedure is implemented as training two successive learners. First, decision list is used to learn the parsing-based NE rules. Then, a Hidden Markov Model is trained on a corpus automatically tagged by the first learner. The resulting NE system approaches supervised NE performance for some NE types.

bootstrapping approach

successive learners

bootstrapping procedure

successive learners

Hidden Markov Model

NE system

Named Entity -LRB- NE -RRB- tagging

supervised NE

concept-based seeds

NE

PERSON NE

decision list

parsing-based NE rules

approach

learner

bootstrapping approach

Named Entity -LRB- NE -RRB- tagging

concept-based seeds

bootstrapping approach

successive learners

bootstrapping approach

successive learners

bootstrapping procedure

decision list

parsing-based NE rules

NE system

supervised NE

concept-based seeds

successive learners

PERSON NE

NE


Input: We present the first known empirical test of an increasingly common speculative claim, by evaluating a representative Chinese-to-English SMT model directly on word sense disambiguation performance, using standard WSD evaluation methodology and datasets from the Senseval-3 Chinese lexical sample task. Much effort has been put in designing and evaluating dedicated word sense disambiguation -LRB- WSD -RRB- models, in particular with the Senseval series of workshops. At the same time, the recent improvements in the BLEU scores of statistical machine translation -LRB- SMT -RRB- suggests that SMT models are good at predicting the right translation of the words in source language sentences. Surprisingly however, the WSD accuracy of SMT models has never been evaluated and compared with that of the dedicated WSD models. We present controlled experiments showing the WSD accuracy of current typical SMT models to be significantly lower than that of all the dedicated WSD models considered. This tends to support the view that despite recent speculative claims to the contrary, current SMT models do have limitations in comparison with dedicated WSD models, and that SMT should benefit from the better predictions made by the WSD models.

Chinese-to-English SMT model

WSD evaluation methodology

dedicated word sense disambiguation -LRB- WSD -RRB- models

SMT models

SMT models

dedicated WSD models

SMT models

dedicated WSD models

SMT models

dedicated WSD models

SMT

WSD models

word sense disambiguation

statistical machine translation -LRB- SMT -RRB-

translation

Senseval-3 Chinese lexical sample task

Senseval series of workshops

BLEU scores

WSD accuracy

WSD accuracy

that

that

word sense disambiguation

Chinese-to-English SMT model

WSD evaluation methodology

Chinese-to-English SMT model

Senseval-3 Chinese lexical sample task

Chinese-to-English SMT model

Senseval series of workshops

dedicated word sense disambiguation -LRB- WSD -RRB- models

BLEU scores

statistical machine translation -LRB- SMT -RRB-

WSD accuracy

SMT models

WSD accuracy

SMT models

SMT models

translation

WSD models

SMT

that

WSD accuracy

that

WSD accuracy

SMT models

dedicated WSD models


Input: In this paper we present a novel, customizable: IE paradigm that takes advantage of predicate-argument structures. We also introduce a new way of automatically identifying predicate argument structures, which is central to our IE paradigm. It is based on: -LRB- 1 -RRB- an extended set of features; and -LRB- 2 -RRB- inductive decision tree learning. The experimental results prove our claim that accurate predicate-argument structures enable high quality IE results.

IE paradigm

IE paradigm

inductive decision tree learning

predicate-argument structures

features

predicate-argument structures

automatically identifying predicate argument structures

IE

It

predicate-argument structures

IE paradigm

features

It

inductive decision tree learning

It

predicate-argument structures

IE

features

inductive decision tree learning


Input: In this paper we present a statistical profile of the Named Entity task, a specific information extraction task for which corpora in several languages are available. Using the results of the statistical analysis, we propose an algorithm for lower bound estimation for Named Entity corpora and discuss the significance of the cross-lingual comparisons provided by the analysis.

statistical profile

algorithm

analysis

Named Entity task

information extraction task

statistical analysis

lower bound estimation

Named Entity corpora

statistical profile

Named Entity task

statistical analysis

algorithm

algorithm

lower bound estimation

lower bound estimation

Named Entity corpora

Named Entity task

information extraction task


Input: We attack an inexplicably under-explored language genre of spoken language -- lyrics in music -- via completely unsuper-vised induction of an SMT-style stochastic transduction grammar for hip hop lyrics, yielding a fully-automatically learned challenge-response system that produces rhyming lyrics given an input. Unlike previous efforts, we choose the domain of hip hop lyrics, which is particularly unstructured and noisy. A novel feature of our approach is that it is completely unsupervised and requires no a priori linguistic or phonetic knowledge. In spite of the level of difficulty of the challenge, the model nevertheless produces fluent output as judged by human evaluators, and performs significantly better than widely used phrase-based SMT models upon the same task.

under-explored language genre of spoken language

lyrics in music

hip hop lyrics

rhyming lyrics

hip hop lyrics

unsuper-vised induction

SMT-style stochastic transduction grammar

fully-automatically learned challenge-response system

phrase-based SMT models

approach

model

task

priori linguistic or phonetic knowledge

human evaluators

lyrics in music

under-explored language genre of spoken language

unsuper-vised induction

under-explored language genre of spoken language

unsuper-vised induction

SMT-style stochastic transduction grammar

unsuper-vised induction

fully-automatically learned challenge-response system

fully-automatically learned challenge-response system

rhyming lyrics

hip hop lyrics

SMT-style stochastic transduction grammar

model

phrase-based SMT models

task

model

task

phrase-based SMT models


Input: In this paper, we investigate the problem of automatically predicting segment boundaries in spoken multiparty dialogue. We extend prior work in two ways. We first apply approaches that have been proposed for predicting top-level topic shifts to the problem of identifying subtopic boundaries. We then explore the impact on performance of using ASR output as opposed to human transcription. Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: -LRB- 1 -RRB- for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, -LRB- 2 -RRB- for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and -LRB- 3 -RRB- conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task. We also find that the transcription errors inevitable in ASR output have a negative impact on models that combine lexical-cohesion and conversational features, but do not change the general preference of approach for the two tasks.

predicting segment boundaries

predicting top-level topic shifts

identifying subtopic boundaries

predicting top-level and predicting subtopic boundaries

predicting subtopic boundaries

predicting top-level boundaries

spoken multiparty dialogue

approaches

tasks

indicators

top-level prediction task

models

tasks

ASR output

human transcription

features

lexical-cohesion and conversational features

conversational cues

cue phrases

overlapping speech

transcription errors

ASR output

lexical-cohesion and conversational features

lexical cohesion-based approach

machine learning approach

spoken multiparty dialogue

predicting segment boundaries

approaches

predicting top-level topic shifts

approaches

identifying subtopic boundaries

lexical cohesion-based approach

predicting subtopic boundaries

machine learning approach

predicting top-level boundaries

indicators

top-level prediction task

predicting top-level topic shifts

identifying subtopic boundaries

transcription errors

ASR output

ASR output

human transcription

predicting subtopic boundaries

predicting top-level and predicting subtopic boundaries

predicting top-level boundaries

predicting top-level and predicting subtopic boundaries

lexical-cohesion and conversational features

machine learning approach

overlapping speech

cue phrases

models

lexical-cohesion and conversational features

cue phrases

conversational cues

overlapping speech

conversational cues


Input: We describe a simple unsupervised technique for learning morphology by identifying hubs in an automaton. For our purposes, a hub is a node in a graph with in-degree greater than one and out-degree greater than one. We create a word-trie, transform it into a minimal DFA, then identify hubs. Those hubs mark the boundary between root and suffix, achieving similar performance to more complex mixtures of techniques.

unsupervised technique

word-trie

morphology

hubs

automaton

hub

node

graph

minimal DFA

hubs

hubs

root

suffix

it

unsupervised technique

morphology

hubs

unsupervised technique

word-trie

hubs

minimal DFA

hubs

hubs

automaton

node

graph

hub

node


Input: In Bayesian machine learning, conjugate priors are popular, mostly due to mathematical convenience. In this paper, we show that there are deeper reasons for choosing a conjugate prior. Specifically, we formulate the conjugate prior in the form of Bregman divergence and show that it is the inherent geometry of conjugate priors that makes them appropriate and intuitive. This geometric interpretation allows one to view the hyperparameters of conjugate priors as the effective sample points, thus providing additional intuition. We use this geometric understanding of conjugate priors to derive the hyperparameters and expression of the prior used to couple the generative and discriminative components of a hybrid model for semi-supervised learning.

Bayesian machine learning

semi-supervised learning

conjugate priors

conjugate prior

conjugate prior

Bregman divergence

conjugate priors

conjugate priors

geometric understanding of conjugate priors

conjugate priors

hyperparameters

prior

generative and discriminative components

hybrid model

conjugate priors

Bayesian machine learning

generative and discriminative components

hybrid model

Bregman divergence

conjugate prior

geometric understanding of conjugate priors

hyperparameters

prior

generative and discriminative components

hybrid model

semi-supervised learning


Input: This paper defines a generative probabilistic model of parse trees, which we call PCFG-LA. This model is an extension of PCFG in which non-terminal symbols are augmented with latent variables. Finegrained CFG rules are automatically induced from a parsed corpus by training a PCFG-LA model using an EM-algorithm. Because exact parsing with a PCFG-LA is NP-hard, several approximations are described and empirically compared. In experiments using the Penn WSJ corpus, our automatically trained model gave a performance of 86.6% -LRB- F1, sentences < 40 words -RRB-, which is comparable to that of an unlexicalized PCFG parser created using extensive manual feature selection.

generative probabilistic model of parse trees

PCFG-LA

PCFG

PCFG-LA model

EM-algorithm

PCFG-LA

unlexicalized PCFG parser

manual feature selection

model

model

non-terminal symbols

latent variables

Finegrained CFG rules

parsed corpus

Penn WSJ corpus

exact parsing

PCFG-LA

generative probabilistic model of parse trees

PCFG

model

latent variables

non-terminal symbols

parsed corpus

Finegrained CFG rules

EM-algorithm

PCFG-LA model

PCFG-LA

exact parsing

manual feature selection

unlexicalized PCFG parser

non-terminal symbols

PCFG

Penn WSJ corpus

model

Penn WSJ corpus

unlexicalized PCFG parser

model

unlexicalized PCFG parser

model

unlexicalized PCFG parser


Input: This paper reports on two contributions to large vocabulary continuous speech recognition. First, we present a new paradigm for speaker-independent -LRB- SI -RRB- training of hidden Markov models -LRB- HMM -RRB-, which uses a large amount of speech from a few speakers instead of the traditional practice of using a little speech from many speakers. In addition, combination of the training speakers is done by averaging the statistics of independently trained models rather than the usual pooling of all the speech data from many speakers prior to training. With only 12 training speakers for SI recognition, we achieved a 7.5% word error rate on a standard grammar and test set from the DARPA Resource Management corpus. This performance is comparable to our best condition for this test suite, using 109 training speakers. Second, we show a significant improvement for speaker adaptation -LRB- SA -RRB- using the new SI corpus and a small amount of speech from the new -LRB- target -RRB- speaker. A probabilistic spectral mapping is estimated independently for each training -LRB- reference -RRB- speaker and the target speaker. Each reference model is transformed to the space of the target speaker and combined by averaging. Using only 40 utterances from the target speaker for adaptation, the error rate dropped to 4.1% -- a 45% reduction in error compared to the SI result.

large vocabulary continuous speech recognition

speaker-independent -LRB- SI -RRB- training of hidden Markov models -LRB- HMM -RRB-

SI recognition

speaker adaptation -LRB- SA -RRB-

adaptation

SI

speech

DARPA Resource Management corpus

SI corpus

averaging the statistics of independently trained models

pooling of all the speech data

word error rate

error rate

probabilistic spectral mapping

reference model

speech

speaker-independent -LRB- SI -RRB- training of hidden Markov models -LRB- HMM -RRB-

averaging the statistics of independently trained models

pooling of all the speech data

word error rate

SI recognition

DARPA Resource Management corpus

SI recognition

SI corpus

speaker adaptation -LRB- SA -RRB-

error rate

adaptation


Input: In this paper, we show how to construct a transfer dictionary automatically. Dictionary construction, one of the most difficult tasks in developing a machine translation system, is expensive. To avoid this problem, we investigate how we build a dictionary using existing linguistic resources. Our algorithm can be applied to any language pairs, but for the present we focus on building a Korean-to-Japanese dictionary using English as a pivot. We attempt three ways of automatic construction to corroborate the effect of the directionality of dictionaries. First, we introduce ``one-time look up'' method using a Korean-to-English and a Japanese-to-English dictionary. Second, we show a method using ``overlapping constraint'' with a Korean-to-English dictionary and an English-to-Japanese dictionary. Third, we consider another alternative method rarely used for building a dictionary: an English-to-Korean dictionary and English-to-Japanese dictionary. We found that the first method is the most effective and the best result can be obtained from combining the three methods.

transfer dictionary

linguistic resources

Korean-to-Japanese dictionary

English

Korean-to-English and a Japanese-to-English dictionary

Korean-to-English dictionary

English-to-Japanese dictionary

English-to-Korean dictionary

English-to-Japanese dictionary

Dictionary construction

machine translation system

automatic construction

dictionary

dictionaries

method

method

dictionary

method

directionality of dictionaries

``overlapping constraint''

``one-time look up'' method

Dictionary construction

machine translation system

linguistic resources

dictionary

English

Korean-to-Japanese dictionary

Korean-to-English and a Japanese-to-English dictionary

``one-time look up'' method

``overlapping constraint''

method

Korean-to-English dictionary

method

English-to-Japanese dictionary

method

method

dictionary

automatic construction

directionality of dictionaries

Korean-to-English dictionary

English-to-Japanese dictionary

English-to-Korean dictionary

English-to-Japanese dictionary

English-to-Korean dictionary

dictionary

English-to-Japanese dictionary

dictionary


Input: An empirical comparison of CFG filtering techniques for LTAG and HPSG is presented. We demonstrate that an approximation of HPSG produces a more effective CFG filter than that of LTAG. We also investigate the reason for that difference.

CFG filtering techniques

LTAG

HPSG

approximation of HPSG

HPSG

CFG filter

LTAG

that

CFG filtering techniques

LTAG

CFG filtering techniques

HPSG

approximation of HPSG

CFG filter

that

LTAG

LTAG

HPSG

CFG filter

that


Input: Syntax-based statistical machine translation -LRB- MT -RRB- aims at applying statistical models to structured data. In this paper, we present a syntax-based statistical machine translation system based on a probabilistic synchronous dependency insertion grammar. Synchronous dependency insertion grammars are a version of synchronous grammars defined on dependency trees. We first introduce our approach to inducing such a grammar from parallel corpora. Second, we describe the graphical model for the machine translation task, which can also be viewed as a stochastic tree-to-tree transducer. We introduce a polynomial time decoding algorithm for the model. We evaluate the outputs of our MT system using the NIST and Bleu automatic MT evaluation software. The result shows that our system outperforms the baseline system based on the IBM models in both translation speed and quality.

Syntax-based statistical machine translation -LRB- MT -RRB-

syntax-based statistical machine translation system

machine translation task

statistical models

probabilistic synchronous dependency insertion grammar

Synchronous dependency insertion grammars

synchronous grammars

grammar

graphical model

stochastic tree-to-tree transducer

polynomial time decoding algorithm

MT system

IBM models

structured data

parallel corpora

dependency trees

approach

model

system

baseline system

NIST and Bleu automatic MT evaluation software

translation speed and quality

statistical models

Syntax-based statistical machine translation -LRB- MT -RRB-

structured data

statistical models

probabilistic synchronous dependency insertion grammar

syntax-based statistical machine translation system

approach

grammar

parallel corpora

grammar

graphical model

machine translation task

stochastic tree-to-tree transducer

graphical model

polynomial time decoding algorithm

model

NIST and Bleu automatic MT evaluation software

MT system

IBM models

baseline system

Synchronous dependency insertion grammars

synchronous grammars

dependency trees

Synchronous dependency insertion grammars

system

baseline system

translation speed and quality

system

translation speed and quality

baseline system


Input: We propose a framework to derive the distance between concepts from distributional measures of word co-occurrences. We use the categories in a published thesaurus as coarse-grained concepts, allowing all possible distance values to be stored in a concept-concept matrix roughly .01% the size of that created by existing measures. We show that the newly proposed concept-distance measures outperform traditional distributional word-distance measures in the tasks of -LRB- 1 -RRB- ranking word pairs in order of semantic distance, and -LRB- 2 -RRB- correcting real-word spelling errors. In the latter task, of all the WordNet-based measures, only that proposed by Jiang and Conrath outperforms the best distributional concept-distance measures.

framework

tasks

task

concepts

coarse-grained concepts

concept-concept matrix

distributional measures of word co-occurrences

concept-distance measures

distributional word-distance measures

WordNet-based measures

distributional concept-distance measures

ranking word pairs in order of semantic distance

correcting real-word spelling errors

distributional measures of word co-occurrences

framework

concept-distance measures

tasks

distributional word-distance measures

tasks

distributional word-distance measures

concept-distance measures

distributional concept-distance measures

WordNet-based measures

ranking word pairs in order of semantic distance

tasks

correcting real-word spelling errors

tasks

correcting real-word spelling errors

ranking word pairs in order of semantic distance

task

WordNet-based measures

task

distributional concept-distance measures


Input: We focus on the problem of building large repositories of lexical conceptual structure -LRB- LCS -RRB- representations for verbs in multiple languages. One of the main results of this work is the definition of a relation between broad semantic classes and LCS meaning components. Our acquisition program-LEXICALL-takes, as input, the result of previous work on verb classification and thematic grid tagging, and outputs LCS representations for different languages. These representations have been ported into English, Arabic and Spanish lexicons, each containing approximately 9000 verbs. We are currently using these lexicons in an operational foreign language tutoring and machine translation.

lexical conceptual structure -LRB- LCS -RRB- representations

LCS meaning components

acquisition program-LEXICALL-

LCS representations

broad semantic classes

English, Arabic and Spanish lexicons

verb classification

thematic grid tagging

operational foreign language tutoring

machine translation

representations

lexicons

broad semantic classes

LCS meaning components

verb classification

thematic grid tagging

operational foreign language tutoring

machine translation

acquisition program-LEXICALL-

LCS representations

verb classification

acquisition program-LEXICALL-

thematic grid tagging

acquisition program-LEXICALL-

representations

English, Arabic and Spanish lexicons

lexicons

operational foreign language tutoring

lexicons

machine translation