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[[ It ]] also gives significant speedups in << inference >> for several datasets with varying degrees of spatio-temporal continuity .

<< It >> also gives significant speedups in inference for several [[ datasets ]] with varying degrees of spatio-temporal continuity .

It also gives significant speedups in inference for several << datasets >> with varying degrees of [[ spatio-temporal continuity ]] .

We also discuss the strengths and weaknesses of our [[ strategy ]] relative to existing << hierarchical approaches >> , and the kinds of image and video data that provide the best speedups .

Motivated by the success of [[ ensemble methods ]] in << machine learning >> and other areas of natural language processing , we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora .

Motivated by the success of [[ ensemble methods ]] in machine learning and other areas of << natural language processing >> , we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora .

Motivated by the success of ensemble methods in machine learning and other areas of natural language processing , we developed a [[ multi-strategy and multi-source approach ]] to << question answering >> which is based on combining the results from different answering agents searching for answers in multiple corpora .

The << answering agents >> adopt fundamentally different [[ strategies ]] , one utilizing primarily knowledge-based mechanisms and the other adopting statistical techniques .

The answering agents adopt fundamentally different << strategies >> , [[ one ]] utilizing primarily knowledge-based mechanisms and the other adopting statistical techniques .

The answering agents adopt fundamentally different strategies , << one >> utilizing primarily [[ knowledge-based mechanisms ]] and the other adopting statistical techniques .

The answering agents adopt fundamentally different << strategies >> , one utilizing primarily knowledge-based mechanisms and the [[ other ]] adopting statistical techniques .

The answering agents adopt fundamentally different strategies , one utilizing primarily knowledge-based mechanisms and the << other >> adopting [[ statistical techniques ]] .

We present our << multi-level answer resolution algorithm >> that combines results from the [[ answering agents ]] at the question , passage , and/or answer levels .

Experiments evaluating the effectiveness of our [[ answer resolution algorithm ]] show a 35.0 % relative improvement over our << baseline system >> in the number of questions correctly answered , and a 32.8 % improvement according to the average precision metric .

Experiments evaluating the effectiveness of our << answer resolution algorithm >> show a 35.0 % relative improvement over our baseline system in the number of questions correctly answered , and a 32.8 % improvement according to the [[ average precision metric ]] .

Experiments evaluating the effectiveness of our answer resolution algorithm show a 35.0 % relative improvement over our << baseline system >> in the number of questions correctly answered , and a 32.8 % improvement according to the [[ average precision metric ]] .

[[ Word Identification ]] has been an important and active issue in << Chinese Natural Language Processing >> .

In this paper , a new [[ mechanism ]] , based on the concept of sublanguage , is proposed for identifying << unknown words >> , especially personal names , in Chinese newspapers .

In this paper , a new << mechanism >> , based on the concept of [[ sublanguage ]] , is proposed for identifying unknown words , especially personal names , in Chinese newspapers .

In this paper , a new mechanism , based on the concept of sublanguage , is proposed for identifying << unknown words >> , especially [[ personal names ]] , in Chinese newspapers .

In this paper , a new << mechanism >> , based on the concept of sublanguage , is proposed for identifying unknown words , especially personal names , in [[ Chinese newspapers ]] .

The proposed << mechanism >> includes [[ title-driven name recognition ]] , adaptive dynamic word formation , identification of 2-character and 3-character Chinese names without title .

The proposed mechanism includes [[ title-driven name recognition ]] , << adaptive dynamic word formation >> , identification of 2-character and 3-character Chinese names without title .

The proposed << mechanism >> includes title-driven name recognition , [[ adaptive dynamic word formation ]] , identification of 2-character and 3-character Chinese names without title .

The proposed mechanism includes title-driven name recognition , [[ adaptive dynamic word formation ]] , << identification of 2-character and 3-character Chinese names without title >> .

The proposed << mechanism >> includes title-driven name recognition , adaptive dynamic word formation , [[ identification of 2-character and 3-character Chinese names without title ]] .

This report describes [[ Paul ]] , a << computer text generation system >> designed to create cohesive text through the use of lexical substitutions .

This report describes Paul , a [[ computer text generation system ]] designed to create << cohesive text >> through the use of lexical substitutions .

This report describes << Paul >> , a computer text generation system designed to create cohesive text through the use of [[ lexical substitutions ]] .

Specifically , this system is designed to deterministically choose between [[ pronominalization ]] , << superordinate substitution >> , and definite noun phrase reiteration .

Specifically , this system is designed to deterministically choose between pronominalization , [[ superordinate substitution ]] , and << definite noun phrase reiteration >> .

The [[ system ]] identifies a strength of << antecedence recovery >> for each of the lexical substitutions .

The system identifies a strength of [[ antecedence recovery ]] for each of the << lexical substitutions >> .

It describes the automated training and evaluation of an Optimal Position Policy , a [[ method ]] of locating the likely << positions of topic-bearing sentences >> based on genre-specific regularities of discourse structure .

It describes the automated training and evaluation of an Optimal Position Policy , a << method >> of locating the likely positions of topic-bearing sentences based on [[ genre-specific regularities of discourse structure ]] .

This [[ method ]] can be used in << applications >> such as information retrieval , routing , and text summarization .

This method can be used in << applications >> such as [[ information retrieval ]] , routing , and text summarization .

This method can be used in applications such as [[ information retrieval ]] , << routing >> , and text summarization .

This method can be used in << applications >> such as information retrieval , [[ routing ]] , and text summarization .

This method can be used in applications such as information retrieval , [[ routing ]] , and << text summarization >> .

This method can be used in << applications >> such as information retrieval , routing , and [[ text summarization ]] .

We describe a general [[ framework ]] for << online multiclass learning >> based on the notion of hypothesis sharing .

We describe a general << framework >> for online multiclass learning based on the [[ notion of hypothesis sharing ]] .

We generalize the [[ multiclass Perceptron ]] to our << framework >> and derive a unifying mistake bound analysis .

We demonstrate the merits of our approach by comparing [[ it ]] to previous << methods >> on both synthetic and natural datasets .

We demonstrate the merits of our approach by comparing << it >> to previous methods on both [[ synthetic and natural datasets ]] .

We demonstrate the merits of our approach by comparing it to previous << methods >> on both [[ synthetic and natural datasets ]] .

We describe a set of [[ supervised machine learning ]] experiments centering on the construction of << statistical models of WH-questions >> .

These << models >> , which are built from [[ shallow linguistic features of questions ]] , are employed to predict target variables which represent a user 's informational goals .

We argue in favor of the the use of [[ labeled directed graph ]] to represent various types of << linguistic structures >> , and illustrate how this allows one to view NLP tasks as graph transformations .

We argue in favor of the the use of [[ labeled directed graph ]] to represent various types of linguistic structures , and illustrate how this allows one to view << NLP tasks >> as graph transformations .

We argue in favor of the the use of labeled directed graph to represent various types of linguistic structures , and illustrate how [[ this ]] allows one to view << NLP tasks >> as graph transformations .

We present a general [[ method ]] for learning such << transformations >> from an annotated corpus and describe experiments with two applications of the method : identification of non-local depenencies -LRB- using Penn Treebank data -RRB- and semantic role labeling -LRB- using Proposition Bank data -RRB- .

We present a general << method >> for learning such transformations from an [[ annotated corpus ]] and describe experiments with two applications of the method : identification of non-local depenencies -LRB- using Penn Treebank data -RRB- and semantic role labeling -LRB- using Proposition Bank data -RRB- .

We present a general method for learning such transformations from an annotated corpus and describe experiments with two << applications >> of the [[ method ]] : identification of non-local depenencies -LRB- using Penn Treebank data -RRB- and semantic role labeling -LRB- using Proposition Bank data -RRB- .

We present a general method for learning such transformations from an annotated corpus and describe experiments with two << applications >> of the method : [[ identification of non-local depenencies ]] -LRB- using Penn Treebank data -RRB- and semantic role labeling -LRB- using Proposition Bank data -RRB- .

We present a general method for learning such transformations from an annotated corpus and describe experiments with two applications of the method : << identification of non-local depenencies >> -LRB- using [[ Penn Treebank data ]] -RRB- and semantic role labeling -LRB- using Proposition Bank data -RRB- .

We present a general method for learning such transformations from an annotated corpus and describe experiments with two << applications >> of the method : identification of non-local depenencies -LRB- using Penn Treebank data -RRB- and [[ semantic role labeling ]] -LRB- using Proposition Bank data -RRB- .

We present a general method for learning such transformations from an annotated corpus and describe experiments with two applications of the method : identification of non-local depenencies -LRB- using Penn Treebank data -RRB- and << semantic role labeling >> -LRB- using [[ Proposition Bank data ]] -RRB- .

We describe a generative probabilistic model of natural language , which we call HBG , that takes advantage of detailed [[ linguistic information ]] to resolve << ambiguity >> .

[[ HBG ]] incorporates lexical , syntactic , semantic , and structural information from the parse tree into the << disambiguation process >> in a novel way .

<< HBG >> incorporates [[ lexical , syntactic , semantic , and structural information ]] from the parse tree into the disambiguation process in a novel way .

We use a [[ corpus of bracketed sentences ]] , called a Treebank , in combination with << decision tree building >> to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence .

We use a [[ corpus of bracketed sentences ]] , called a Treebank , in combination with decision tree building to tease out the relevant aspects of a << parse tree >> that will determine the correct parse of a sentence .

We use a corpus of bracketed sentences , called a Treebank , in combination with [[ decision tree building ]] to tease out the relevant aspects of a << parse tree >> that will determine the correct parse of a sentence .

We use a corpus of bracketed sentences , called a Treebank , in combination with decision tree building to tease out the relevant aspects of a [[ parse tree ]] that will determine the correct << parse >> of a sentence .

This stands in contrast to the usual approach of further [[ grammar tailoring ]] via the usual linguistic introspection in the hope of generating the correct << parse >> .

This stands in contrast to the usual approach of further << grammar tailoring >> via the usual [[ linguistic introspection ]] in the hope of generating the correct parse .

In head-to-head tests against one of the best existing << robust probabilistic parsing models >> , which we call [[ P-CFG ]] , the HBG model significantly outperforms P-CFG , increasing the parsing accuracy rate from 60 % to 75 % , a 37 % reduction in error .

In head-to-head tests against one of the best existing robust probabilistic parsing models , which we call P-CFG , the [[ HBG model ]] significantly outperforms << P-CFG >> , increasing the parsing accuracy rate from 60 % to 75 % , a 37 % reduction in error .

In head-to-head tests against one of the best existing robust probabilistic parsing models , which we call P-CFG , the << HBG model >> significantly outperforms P-CFG , increasing the [[ parsing accuracy rate ]] from 60 % to 75 % , a 37 % reduction in error .

The framework of the << analysis >> is [[ model-theoretic semantics ]] .

This paper addresses the issue of << word-sense ambiguity >> in extraction from [[ machine-readable resources ]] for the construction of large-scale knowledge sources .

This paper addresses the issue of word-sense ambiguity in extraction from [[ machine-readable resources ]] for the << construction of large-scale knowledge sources >> .

We describe two experiments : one which ignored word-sense distinctions , resulting in 6.3 % [[ accuracy ]] for << semantic classification >> of verbs based on -LRB- Levin , 1993 -RRB- ; and one which exploited word-sense distinctions , resulting in 97.9 % accuracy .

These experiments were dual purpose : -LRB- 1 -RRB- to validate the central thesis of the work of -LRB- Levin , 1993 -RRB- , i.e. , that [[ verb semantics ]] and << syntactic behavior >> are predictably related ; -LRB- 2 -RRB- to demonstrate that a 15-fold improvement can be achieved in deriving semantic information from syntactic cues if we first divide the syntactic cues into distinct groupings that correlate with different word senses .

These experiments were dual purpose : -LRB- 1 -RRB- to validate the central thesis of the work of -LRB- Levin , 1993 -RRB- , i.e. , that verb semantics and syntactic behavior are predictably related ; -LRB- 2 -RRB- to demonstrate that a 15-fold improvement can be achieved in deriving << semantic information >> from [[ syntactic cues ]] if we first divide the syntactic cues into distinct groupings that correlate with different word senses .

Finally , we show that we can provide effective acquisition [[ techniques ]] for novel << word senses >> using a combination of online sources .

Finally , we show that we can provide effective acquisition << techniques >> for novel word senses using a combination of [[ online sources ]] .

The [[ TIPSTER Architecture ]] has been designed to enable a variety of different << text applications >> to use a set of common text processing modules .

The TIPSTER Architecture has been designed to enable a variety of different << text applications >> to use a set of [[ common text processing modules ]] .

Since [[ user interfaces ]] work best when customized for particular << applications >> , it is appropriator that no particular user interface styles or conventions are described in the TIPSTER Architecture specification .

However , the Computing Research Laboratory -LRB- CRL -RRB- has constructed several << TIPSTER applications >> that use a common set of configurable [[ Graphical User Interface -LRB- GUI -RRB- functions ]] .

These << GUIs >> were constructed using [[ CRL 's TIPSTER User Interface Toolkit -LRB- TUIT -RRB- ]] .

[[ TUIT ]] is a << software library >> that can be used to construct multilingual TIPSTER user interfaces for a set of common user tasks .

[[ TUIT ]] is a software library that can be used to construct << multilingual TIPSTER user interfaces >> for a set of common user tasks .

CRL developed [[ TUIT ]] to support their work to integrate << TIPSTER modules >> for the 6 and 12 month TIPSTER II demonstrations as well as their Oleada and Temple demonstration projects .

While such decoding is an essential underpinning , much recent work suggests that natural language interfaces will never appear cooperative or graceful unless << they >> also incorporate numerous [[ non-literal aspects of communication ]] , such as robust communication procedures .

While such decoding is an essential underpinning , much recent work suggests that natural language interfaces will never appear cooperative or graceful unless they also incorporate numerous << non-literal aspects of communication >> , such as [[ robust communication procedures ]] .

This paper defends that view , but claims that direct imitation of human performance is not the best way to implement many of these non-literal aspects of communication ; that the new technology of powerful << personal computers >> with integral [[ graphics displays ]] offers techniques superior to those of humans for these aspects , while still satisfying human communication needs .

This paper proposes a framework in which [[ Lagrangian Particle Dynamics ]] is used for the << segmentation of high density crowd flows >> and detection of flow instabilities .

This paper proposes a framework in which [[ Lagrangian Particle Dynamics ]] is used for the segmentation of high density crowd flows and << detection of flow instabilities >> .

This paper proposes a framework in which Lagrangian Particle Dynamics is used for the [[ segmentation of high density crowd flows ]] and << detection of flow instabilities >> .

For this purpose , a << flow field >> generated by a [[ moving crowd ]] is treated as an aperiodic dynamical system .

For this purpose , a << flow field >> generated by a moving crowd is treated as an [[ aperiodic dynamical system ]] .

A [[ grid of particles ]] is overlaid on the << flow field >> , and is advected using a numerical integration scheme .

A << grid of particles >> is overlaid on the flow field , and is advected using a [[ numerical integration scheme ]] .

The << evolution of particles >> through the flow is tracked using a [[ Flow Map ]] , whose spatial gradients are subsequently used to setup a Cauchy Green Deformation tensor for quantifying the amount by which the neighboring particles have diverged over the length of the integration .

The evolution of particles through the flow is tracked using a Flow Map , whose [[ spatial gradients ]] are subsequently used to setup a << Cauchy Green Deformation tensor >> for quantifying the amount by which the neighboring particles have diverged over the length of the integration .

The [[ maximum eigenvalue ]] of the << tensor >> is used to construct a Finite Time Lyapunov Exponent -LRB- FTLE -RRB- field , which reveals the Lagrangian Coherent Structures -LRB- LCS -RRB- present in the underlying flow .