We present an efficient algorithm for << chart-based phrase structure parsing >> of [[ natural language ]] that is tailored to the problem of extracting specific information from unrestricted texts where many of the words are unknown and much of the text is irrelevant to the task .


This is facilitated through the use of << phrase boundary heuristics >> based on the placement of [[ function words ]] , and by heuristic rules that permit certain kinds of phrases to be deduced despite the presence of unknown words .


A further << reduction in the search space >> is achieved by using [[ semantic ]] rather than syntactic categories on the terminal and non-terminal edges , thereby reducing the amount of ambiguity and thus the number of edges , since only edges with a valid semantic interpretation are ever introduced .


A further reduction in the search space is achieved by using [[ semantic ]] rather than << syntactic categories >> on the terminal and non-terminal edges , thereby reducing the amount of ambiguity and thus the number of edges , since only edges with a valid semantic interpretation are ever introduced .


A further reduction in the search space is achieved by using [[ semantic ]] rather than syntactic categories on the << terminal and non-terminal edges >> , thereby reducing the amount of ambiguity and thus the number of edges , since only edges with a valid semantic interpretation are ever introduced .


A further reduction in the search space is achieved by using semantic rather than [[ syntactic categories ]] on the << terminal and non-terminal edges >> , thereby reducing the amount of ambiguity and thus the number of edges , since only edges with a valid semantic interpretation are ever introduced .


[[ Automatic estimation of word significance ]] oriented for << speech-based Information Retrieval -LRB- IR -RRB- >> is addressed .


Since the significance of words differs in IR , << automatic speech recognition -LRB- ASR -RRB- >> performance has been evaluated based on [[ weighted word error rate -LRB- WWER -RRB- ]] , which gives a weight on errors from the viewpoint of IR , instead of word error rate -LRB- WER -RRB- , which treats all words uniformly .


Since the significance of words differs in IR , automatic speech recognition -LRB- ASR -RRB- performance has been evaluated based on << weighted word error rate -LRB- WWER -RRB- >> , which gives a weight on errors from the viewpoint of IR , instead of [[ word error rate -LRB- WER -RRB- ]] , which treats all words uniformly .


A [[ decoding strategy ]] that minimizes << WWER >> based on a Minimum Bayes-Risk framework has been shown , and the reduction of errors on both ASR and IR has been reported .


A << decoding strategy >> that minimizes WWER based on a [[ Minimum Bayes-Risk framework ]] has been shown , and the reduction of errors on both ASR and IR has been reported .


A decoding strategy that minimizes WWER based on a Minimum Bayes-Risk framework has been shown , and the reduction of errors on both [[ ASR ]] and << IR >> has been reported .


In this paper , we propose an [[ automatic estimation method ]] for << word significance -LRB- weights -RRB- >> based on its influence on IR .


Specifically , weights are estimated so that [[ evaluation measures ]] of << ASR >> and IR are equivalent .


Specifically , weights are estimated so that [[ evaluation measures ]] of ASR and << IR >> are equivalent .


Specifically , weights are estimated so that evaluation measures of [[ ASR ]] and << IR >> are equivalent .


We apply the proposed [[ method ]] to a << speech-based information retrieval system >> , which is a typical IR system , and show that the method works well .


We apply the proposed method to a [[ speech-based information retrieval system ]] , which is a typical << IR system >> , and show that the method works well .


[[ Methods ]] developed for << spelling correction >> for languages like English -LRB- see the review by Kukich -LRB- Kukich , 1992 -RRB- -RRB- are not readily applicable to agglutinative languages .


Methods developed for [[ spelling correction ]] for << languages >> like English -LRB- see the review by Kukich -LRB- Kukich , 1992 -RRB- -RRB- are not readily applicable to agglutinative languages .


Methods developed for spelling correction for << languages >> like [[ English ]] -LRB- see the review by Kukich -LRB- Kukich , 1992 -RRB- -RRB- are not readily applicable to agglutinative languages .


This poster presents an [[ approach ]] to << spelling correction >> in agglutinative languages that is based on two-level morphology and a dynamic-programming based search algorithm .


This poster presents an approach to << spelling correction >> in [[ agglutinative languages ]] that is based on two-level morphology and a dynamic-programming based search algorithm .


This poster presents an approach to << spelling correction >> in agglutinative languages that is based on [[ two-level morphology ]] and a dynamic-programming based search algorithm .


This poster presents an approach to spelling correction in agglutinative languages that is based on [[ two-level morphology ]] and a << dynamic-programming based search algorithm >> .


This poster presents an approach to << spelling correction >> in agglutinative languages that is based on two-level morphology and a [[ dynamic-programming based search algorithm ]] .


After an overview of our approach , we present results from experiments with << spelling correction >> in [[ Turkish ]] .


In this paper , we present a novel [[ training method ]] for a << localized phrase-based prediction model >> for statistical machine translation -LRB- SMT -RRB- .


In this paper , we present a novel training method for a [[ localized phrase-based prediction model ]] for << statistical machine translation -LRB- SMT -RRB- >> .


The [[ model ]] predicts blocks with orientation to handle << local phrase re-ordering >> .


We use a [[ maximum likelihood criterion ]] to train a << log-linear block bigram model >> which uses real-valued features -LRB- e.g. a language model score -RRB- as well as binary features based on the block identities themselves , e.g. block bigram features .


We use a maximum likelihood criterion to train a << log-linear block bigram model >> which uses [[ real-valued features ]] -LRB- e.g. a language model score -RRB- as well as binary features based on the block identities themselves , e.g. block bigram features .


We use a maximum likelihood criterion to train a log-linear block bigram model which uses [[ real-valued features ]] -LRB- e.g. a language model score -RRB- as well as << binary features >> based on the block identities themselves , e.g. block bigram features .


We use a maximum likelihood criterion to train a log-linear block bigram model which uses << real-valued features >> -LRB- e.g. a [[ language model score ]] -RRB- as well as binary features based on the block identities themselves , e.g. block bigram features .


We use a maximum likelihood criterion to train a << log-linear block bigram model >> which uses real-valued features -LRB- e.g. a language model score -RRB- as well as [[ binary features ]] based on the block identities themselves , e.g. block bigram features .


Our << training algorithm >> can easily handle millions of [[ features ]] .


The best [[ system ]] obtains a 18.6 % improvement over the << baseline >> on a standard Arabic-English translation task .


The best << system >> obtains a 18.6 % improvement over the baseline on a standard [[ Arabic-English translation task ]] .


The best system obtains a 18.6 % improvement over the << baseline >> on a standard [[ Arabic-English translation task ]] .


In this paper we describe a novel [[ data structure ]] for << phrase-based statistical machine translation >> which allows for the retrieval of arbitrarily long phrases while simultaneously using less memory than is required by current decoder implementations .


In this paper we describe a novel [[ data structure ]] for phrase-based statistical machine translation which allows for the << retrieval of arbitrarily long phrases >> while simultaneously using less memory than is required by current decoder implementations .


We detail the [[ computational complexity ]] and << average retrieval times >> for looking up phrase translations in our suffix array-based data structure .


We detail the computational complexity and average retrieval times for looking up [[ phrase translations ]] in our << suffix array-based data structure >> .


We show how << sampling >> can be used to reduce the [[ retrieval time ]] by orders of magnitude with no loss in translation quality .


We show how << sampling >> can be used to reduce the retrieval time by orders of magnitude with no loss in [[ translation quality ]] .


The major objective of this program is to develop and demonstrate robust , high performance [[ continuous speech recognition -LRB- CSR -RRB- techniques ]] focussed on application in << Spoken Language Systems -LRB- SLS -RRB- >> which will enhance the effectiveness of military and civilian computer-based systems .


The major objective of this program is to develop and demonstrate robust , high performance continuous speech recognition -LRB- CSR -RRB- techniques focussed on application in [[ Spoken Language Systems -LRB- SLS -RRB- ]] which will enhance the effectiveness of << military and civilian computer-based systems >> .


A key complementary objective is to define and develop applications of robust speech recognition and understanding systems , and to help catalyze the transition of [[ spoken language technology ]] into << military and civilian systems >> , with particular focus on application of robust CSR to mobile military command and control .


A key complementary objective is to define and develop applications of robust speech recognition and understanding systems , and to help catalyze the transition of spoken language technology into military and civilian systems , with particular focus on application of robust [[ CSR ]] to << mobile military command and control >> .


The research effort focusses on developing advanced [[ acoustic modelling ]] , << rapid search >> , and recognition-time adaptation techniques for robust large-vocabulary CSR , and on applying these techniques to the new ARPA large-vocabulary CSR corpora and to military application tasks .


The research effort focusses on developing advanced [[ acoustic modelling ]] , rapid search , and recognition-time adaptation techniques for robust << large-vocabulary CSR >> , and on applying these techniques to the new ARPA large-vocabulary CSR corpora and to military application tasks .


The research effort focusses on developing advanced [[ acoustic modelling ]] , rapid search , and recognition-time adaptation techniques for robust large-vocabulary CSR , and on applying these << techniques >> to the new ARPA large-vocabulary CSR corpora and to military application tasks .


The research effort focusses on developing advanced [[ acoustic modelling ]] , rapid search , and recognition-time adaptation techniques for robust large-vocabulary CSR , and on applying these techniques to the new ARPA large-vocabulary CSR corpora and to << military application tasks >> .


The research effort focusses on developing advanced acoustic modelling , [[ rapid search ]] , and << recognition-time adaptation techniques >> for robust large-vocabulary CSR , and on applying these techniques to the new ARPA large-vocabulary CSR corpora and to military application tasks .


The research effort focusses on developing advanced acoustic modelling , [[ rapid search ]] , and recognition-time adaptation techniques for robust << large-vocabulary CSR >> , and on applying these techniques to the new ARPA large-vocabulary CSR corpora and to military application tasks .


The research effort focusses on developing advanced acoustic modelling , [[ rapid search ]] , and recognition-time adaptation techniques for robust large-vocabulary CSR , and on applying these << techniques >> to the new ARPA large-vocabulary CSR corpora and to military application tasks .


The research effort focusses on developing advanced acoustic modelling , [[ rapid search ]] , and recognition-time adaptation techniques for robust large-vocabulary CSR , and on applying these techniques to the new ARPA large-vocabulary CSR corpora and to << military application tasks >> .


The research effort focusses on developing advanced acoustic modelling , rapid search , and [[ recognition-time adaptation techniques ]] for robust << large-vocabulary CSR >> , and on applying these techniques to the new ARPA large-vocabulary CSR corpora and to military application tasks .


The research effort focusses on developing advanced acoustic modelling , rapid search , and [[ recognition-time adaptation techniques ]] for robust large-vocabulary CSR , and on applying these << techniques >> to the new ARPA large-vocabulary CSR corpora and to military application tasks .


The research effort focusses on developing advanced acoustic modelling , rapid search , and [[ recognition-time adaptation techniques ]] for robust large-vocabulary CSR , and on applying these techniques to the new ARPA large-vocabulary CSR corpora and to << military application tasks >> .


The research effort focusses on developing advanced acoustic modelling , rapid search , and recognition-time adaptation techniques for robust [[ large-vocabulary CSR ]] , and on applying these techniques to the new << ARPA large-vocabulary CSR corpora >> and to military application tasks .


The research effort focusses on developing advanced acoustic modelling , rapid search , and recognition-time adaptation techniques for robust large-vocabulary CSR , and on applying these [[ techniques ]] to the new ARPA large-vocabulary CSR corpora and to << military application tasks >> .


The research effort focusses on developing advanced acoustic modelling , rapid search , and recognition-time adaptation techniques for robust large-vocabulary CSR , and on applying these << techniques >> to the new [[ ARPA large-vocabulary CSR corpora ]] and to military application tasks .


This paper examines what kind of << similarity between words >> can be represented by what kind of [[ word vectors ]] in the vector space model .


This paper examines what kind of similarity between words can be represented by what kind of << word vectors >> in the [[ vector space model ]] .


Through two experiments , three [[ methods ]] for << constructing word vectors >> , i.e. , LSA-based , cooccurrence-based and dictionary-based methods , were compared in terms of the ability to represent two kinds of similarity , i.e. , taxonomic similarity and associative similarity .


Through two experiments , three methods for constructing word vectors , i.e. , [[ LSA-based , cooccurrence-based and dictionary-based methods ]] , were compared in terms of the ability to represent two kinds of << similarity >> , i.e. , taxonomic similarity and associative similarity .


Through two experiments , three methods for constructing word vectors , i.e. , LSA-based , cooccurrence-based and dictionary-based methods , were compared in terms of the ability to represent two kinds of << similarity >> , i.e. , [[ taxonomic similarity ]] and associative similarity .


Through two experiments , three methods for constructing word vectors , i.e. , LSA-based , cooccurrence-based and dictionary-based methods , were compared in terms of the ability to represent two kinds of similarity , i.e. , [[ taxonomic similarity ]] and << associative similarity >> .


Through two experiments , three methods for constructing word vectors , i.e. , LSA-based , cooccurrence-based and dictionary-based methods , were compared in terms of the ability to represent two kinds of << similarity >> , i.e. , taxonomic similarity and [[ associative similarity ]] .


The result of the comparison was that the [[ dictionary-based word vectors ]] better reflect << taxonomic similarity >> , while the LSA-based and the cooccurrence-based word vectors better reflect associative similarity .


The result of the comparison was that the dictionary-based word vectors better reflect taxonomic similarity , while the [[ LSA-based and the cooccurrence-based word vectors ]] better reflect << associative similarity >> .


This paper presents a << maximum entropy word alignment algorithm >> for [[ Arabic-English ]] based on supervised training data .


This paper presents a << maximum entropy word alignment algorithm >> for Arabic-English based on [[ supervised training data ]] .


We demonstrate that it is feasible to create [[ training material ]] for problems in << machine translation >> and that a mixture of supervised and unsupervised methods yields superior performance .


The [[ probabilistic model ]] used in the << alignment >> directly models the link decisions .


The [[ probabilistic model ]] used in the alignment directly models the << link decisions >> .


Significant improvement over traditional [[ word alignment techniques ]] is shown as well as improvement on several << machine translation tests >> .


Performance of the [[ algorithm ]] is contrasted with << human annotation >> performance .


In this paper , we propose a novel [[ Cooperative Model ]] for << natural language understanding >> in a dialogue system .


In this paper , we propose a novel Cooperative Model for [[ natural language understanding ]] in a << dialogue system >> .


We build << this >> based on both [[ Finite State Model -LRB- FSM -RRB- ]] and Statistical Learning Model -LRB- SLM -RRB- .


We build this based on both [[ Finite State Model -LRB- FSM -RRB- ]] and << Statistical Learning Model -LRB- SLM -RRB- >> .


We build << this >> based on both Finite State Model -LRB- FSM -RRB- and [[ Statistical Learning Model -LRB- SLM -RRB- ]] .


[[ FSM ]] provides two strategies for << language understanding >> and have a high accuracy but little robustness and flexibility .


The [[ ambiguity resolution of right-side dependencies ]] is essential for << dependency parsing >> of sentences with two or more verbs .


Previous works on shift-reduce dependency parsers may not guarantee the [[ connectivity ]] of a << dependency tree >> due to their weakness at resolving the right-side dependencies .


This paper proposes a << two-phase shift-reduce dependency parser >> based on [[ SVM learning ]] .


The [[ left-side dependents ]] and << right-side nominal dependents >> are detected in Phase I , and right-side verbal dependents are decided in Phase II .


The left-side dependents and << right-side nominal dependents >> are detected in Phase I , and [[ right-side verbal dependents ]] are decided in Phase II .


In experimental evaluation , our proposed [[ method ]] outperforms previous << shift-reduce dependency parsers >> for the Chine language , showing improvement of dependency accuracy by 10.08 % .


In experimental evaluation , our proposed << method >> outperforms previous shift-reduce dependency parsers for the [[ Chine language ]] , showing improvement of dependency accuracy by 10.08 % .


In experimental evaluation , our proposed method outperforms previous << shift-reduce dependency parsers >> for the [[ Chine language ]] , showing improvement of dependency accuracy by 10.08 % .


In experimental evaluation , our proposed << method >> outperforms previous shift-reduce dependency parsers for the Chine language , showing improvement of [[ dependency accuracy ]] by 10.08 % .


In experimental evaluation , our proposed method outperforms previous << shift-reduce dependency parsers >> for the Chine language , showing improvement of [[ dependency accuracy ]] by 10.08 % .


By using [[ commands ]] or << rules >> which are defined to facilitate the construction of format expected or some mathematical expressions , elaborate and pretty documents can be successfully obtained .


By using [[ commands ]] or rules which are defined to facilitate the construction of format expected or some << mathematical expressions >> , elaborate and pretty documents can be successfully obtained .


By using commands or [[ rules ]] which are defined to facilitate the construction of format expected or some << mathematical expressions >> , elaborate and pretty documents can be successfully obtained .


This paper presents an [[ evaluation method ]] employing a latent variable model for << paraphrases >> with their contexts .


This paper presents an << evaluation method >> employing a [[ latent variable model ]] for paraphrases with their contexts .


The results also revealed an upper bound of [[ accuracy ]] of 77 % with the << method >> when using only topic information .


The results also revealed an upper bound of accuracy of 77 % with the << method >> when using only [[ topic information ]] .


We describe the [[ methods ]] and << hardware >> that we are using to produce a real-time demonstration of an integrated Spoken Language System .


We describe the [[ methods ]] and hardware that we are using to produce a real-time demonstration of an << integrated Spoken Language System >> .


We describe the methods and [[ hardware ]] that we are using to produce a real-time demonstration of an << integrated Spoken Language System >> .


We describe [[ algorithms ]] that greatly reduce the computation needed to compute the << N-Best sentence hypotheses >> .


To avoid << grammar coverage problems >> we use a [[ fully-connected first-order statistical class grammar ]] .


The << speech-search algorithm >> is implemented on a [[ board ]] with a single Intel i860 chip , which provides a factor of 5 speed-up over a SUN 4 for straight C code .


The speech-search algorithm is implemented on a << board >> with a single [[ Intel i860 chip ]] , which provides a factor of 5 speed-up over a SUN 4 for straight C code .


The speech-search algorithm is implemented on a board with a single [[ Intel i860 chip ]] , which provides a factor of 5 speed-up over a << SUN 4 >> for straight C code .


The speech-search algorithm is implemented on a board with a single [[ Intel i860 chip ]] , which provides a factor of 5 speed-up over a SUN 4 for << straight C code >> .


The speech-search algorithm is implemented on a board with a single Intel i860 chip , which provides a factor of 5 speed-up over a [[ SUN 4 ]] for << straight C code >> .


The [[ board ]] plugs directly into the VME bus of the SUN4 , which controls the << system >> and contains the natural language system and application back end .


The board plugs directly into the [[ VME bus ]] of the << SUN4 >> , which controls the system and contains the natural language system and application back end .


The << board >> plugs directly into the VME bus of the SUN4 , which controls the system and contains the [[ natural language system ]] and application back end .


The board plugs directly into the VME bus of the SUN4 , which controls the system and contains the [[ natural language system ]] and << application back end >> .


The << board >> plugs directly into the VME bus of the SUN4 , which controls the system and contains the natural language system and [[ application back end ]] .


We address the problem of << estimating location information >> of an [[ image ]] using principles from automated representation learning .


We address the problem of << estimating location information >> of an image using principles from [[ automated representation learning ]] .


We pursue a hierarchical sparse coding approach that learns features useful in discriminating images across locations , by initializing << it >> with a [[ geometric prior ]] corresponding to transformations between image appearance space and their corresponding location grouping space using the notion of parallel transport on manifolds .


We pursue a hierarchical sparse coding approach that learns features useful in discriminating images across locations , by initializing it with a << geometric prior >> corresponding to transformations between image appearance space and their corresponding location grouping space using the notion of [[ parallel transport on manifolds ]] .


We then extend this [[ approach ]] to account for the availability of << heterogeneous data modalities >> such as geo-tags and videos pertaining to different locations , and also study a relatively under-addressed problem of transferring knowledge available from certain locations to infer the grouping of data from novel locations .


We then extend this approach to account for the availability of << heterogeneous data modalities >> such as [[ geo-tags ]] and videos pertaining to different locations , and also study a relatively under-addressed problem of transferring knowledge available from certain locations to infer the grouping of data from novel locations .


We then extend this approach to account for the availability of heterogeneous data modalities such as [[ geo-tags ]] and << videos >> pertaining to different locations , and also study a relatively under-addressed problem of transferring knowledge available from certain locations to infer the grouping of data from novel locations .


We then extend this approach to account for the availability of << heterogeneous data modalities >> such as geo-tags and [[ videos ]] pertaining to different locations , and also study a relatively under-addressed problem of transferring knowledge available from certain locations to infer the grouping of data from novel locations .


We then extend this approach to account for the availability of heterogeneous data modalities such as geo-tags and videos pertaining to different locations , and also study a relatively under-addressed problem of [[ transferring knowledge ]] available from certain locations to infer the << grouping of data >> from novel locations .


We evaluate our << approach >> on several standard [[ datasets ]] such as im2gps , San Francisco and MediaEval2010 , and obtain state-of-the-art results .


We evaluate our approach on several standard << datasets >> such as [[ im2gps ]] , San Francisco and MediaEval2010 , and obtain state-of-the-art results .


We evaluate our approach on several standard datasets such as [[ im2gps ]] , << San Francisco >> and MediaEval2010 , and obtain state-of-the-art results .


We evaluate our approach on several standard << datasets >> such as im2gps , [[ San Francisco ]] and MediaEval2010 , and obtain state-of-the-art results .


We evaluate our approach on several standard datasets such as im2gps , [[ San Francisco ]] and << MediaEval2010 >> , and obtain state-of-the-art results .


We evaluate our approach on several standard << datasets >> such as im2gps , San Francisco and [[ MediaEval2010 ]] , and obtain state-of-the-art results .


Conventional << HMMs >> have [[ weak duration constraints ]] .


In noisy conditions , the mismatch between corrupted speech signals and << models >> trained on [[ clean speech ]] may cause the decoder to produce word matches with unrealistic durations .


In noisy conditions , the mismatch between corrupted speech signals and models trained on clean speech may cause the [[ decoder ]] to produce << word matches >> with unrealistic durations .


In noisy conditions , the mismatch between corrupted speech signals and models trained on clean speech may cause the decoder to produce << word matches >> with [[ unrealistic durations ]] .


This paper presents a simple way to incorporate << word duration constraints >> by [[ unrolling HMMs ]] to form a lattice where word duration probabilities can be applied directly to state transitions .


This paper presents a simple way to incorporate word duration constraints by [[ unrolling HMMs ]] to form a << lattice >> where word duration probabilities can be applied directly to state transitions .


This paper presents a simple way to incorporate word duration constraints by unrolling HMMs to form a lattice where [[ word duration probabilities ]] can be applied directly to << state transitions >> .


The expanded << HMMs >> are compatible with conventional [[ Viterbi decoding ]] .


Experiments on [[ connected-digit recognition ]] show that when using explicit duration constraints the << decoder >> generates word matches with more reasonable durations , and word error rates are significantly reduced across a broad range of noise conditions .


Experiments on connected-digit recognition show that when using explicit [[ duration constraints ]] the << decoder >> generates word matches with more reasonable durations , and word error rates are significantly reduced across a broad range of noise conditions .


Experiments on connected-digit recognition show that when using explicit duration constraints the [[ decoder ]] generates << word matches >> with more reasonable durations , and word error rates are significantly reduced across a broad range of noise conditions .


One of the claimed benefits of [[ Tree Adjoining Grammars ]] is that they have an << extended domain of locality -LRB- EDOL -RRB- >> .


We consider how this can be exploited to limit the need for [[ feature structure unification ]] during << parsing >> .


We compare two wide-coverage << lexicalized grammars of English >> , [[ LEXSYS ]] and XTAG , finding that the two grammars exploit EDOL in different ways .


We compare two wide-coverage lexicalized grammars of English , [[ LEXSYS ]] and << XTAG >> , finding that the two grammars exploit EDOL in different ways .


We compare two wide-coverage << lexicalized grammars of English >> , LEXSYS and [[ XTAG ]] , finding that the two grammars exploit EDOL in different ways .


We compare two wide-coverage lexicalized grammars of English , LEXSYS and XTAG , finding that the two [[ grammars ]] exploit << EDOL >> in different ways .


[[ Identity uncertainty ]] is a pervasive problem in << real-world data analysis >> .


Our << approach >> is based on the use of a [[ relational probability model ]] to define a generative model for the domain , including models of author and title corruption and a probabilistic citation grammar .


Our approach is based on the use of a [[ relational probability model ]] to define a << generative model >> for the domain , including models of author and title corruption and a probabilistic citation grammar .


Our approach is based on the use of a relational probability model to define a [[ generative model ]] for the << domain >> , including models of author and title corruption and a probabilistic citation grammar .


Our approach is based on the use of a << relational probability model >> to define a generative model for the domain , including [[ models of author and title corruption ]] and a probabilistic citation grammar .


Our approach is based on the use of a relational probability model to define a generative model for the domain , including [[ models of author and title corruption ]] and a << probabilistic citation grammar >> .


Our approach is based on the use of a << relational probability model >> to define a generative model for the domain , including models of author and title corruption and a [[ probabilistic citation grammar ]] .


<< Identity uncertainty >> is handled by extending standard [[ models ]] to incorporate probabilities over the possible mappings between terms in the language and objects in the domain .


<< Inference >> is based on [[ Markov chain Monte Carlo ]] , augmented with specific methods for generating efficient proposals when the domain contains many objects .


<< Inference >> is based on Markov chain Monte Carlo , augmented with specific [[ methods ]] for generating efficient proposals when the domain contains many objects .


Results on several [[ citation data sets ]] show that the << method >> outperforms current algorithms for citation matching .


Results on several citation data sets show that the [[ method ]] outperforms << current algorithms >> for citation matching .


Results on several citation data sets show that the [[ method ]] outperforms current algorithms for << citation matching >> .


Results on several citation data sets show that the method outperforms [[ current algorithms ]] for << citation matching >> .


The declarative , relational nature of the model also means that our [[ algorithm ]] can determine << object characteristics >> such as author names by combining multiple citations of multiple papers .


The declarative , relational nature of the model also means that our algorithm can determine << object characteristics >> such as [[ author names ]] by combining multiple citations of multiple papers .


The paper proposes and empirically motivates an integration of [[ supervised learning ]] with unsupervised learning to deal with << human biases in summarization >> .


The paper proposes and empirically motivates an integration of << supervised learning >> with [[ unsupervised learning ]] to deal with human biases in summarization .


The paper proposes and empirically motivates an integration of supervised learning with [[ unsupervised learning ]] to deal with << human biases in summarization >> .


In particular , we explore the use of << probabilistic decision tree >> within the [[ clustering framework ]] to account for the variation as well as regularity in human created summaries .


The << corpus of human created extracts >> is created from a [[ newspaper corpus ]] and used as a test set .


We build probabilistic decision trees of different flavors and integrate each of << them >> with the [[ clustering framework ]] .


In this study , we propose a knowledge-independent method for aligning terms and thus extracting translations from a << small , domain-specific corpus >> consisting of [[ parallel English and Chinese court judgments ]] from Hong Kong .


With a [[ sentence-aligned corpus ]] , << translation equivalences >> are suggested by analysing the frequency profiles of parallel concordances .


With a sentence-aligned corpus , translation equivalences are suggested by analysing the [[ frequency profiles ]] of << parallel concordances >> .


The [[ method ]] overcomes the limitations of conventional << statistical methods >> which require large corpora to be effective , and lexical approaches which depend on existing bilingual dictionaries .


The [[ method ]] overcomes the limitations of conventional statistical methods which require large corpora to be effective , and << lexical approaches >> which depend on existing bilingual dictionaries .


The method overcomes the limitations of conventional << statistical methods >> which require [[ large corpora ]] to be effective , and lexical approaches which depend on existing bilingual dictionaries .


The method overcomes the limitations of conventional statistical methods which require large corpora to be effective , and << lexical approaches >> which depend on existing [[ bilingual dictionaries ]] .


Pilot testing on a parallel corpus of about 113K Chinese words and 120K English words gives an encouraging 85 % [[ precision ]] and 45 % << recall >> .


Future work includes fine-tuning the algorithm upon the analysis of the errors , and acquiring a [[ translation lexicon ]] for << legal terminology >> by filtering out general terms .


Traditional [[ machine learning techniques ]] have been applied to this << problem >> with reasonable success , but they have been shown to work well only when there is a good match between the training and test data with respect to topic .


This paper demonstrates that match with respect to domain and time is also important , and presents preliminary experiments with << training data >> labeled with [[ emoticons ]] , which has the potential of being independent of domain , topic and time .


We present a novel [[ algorithm ]] for estimating the broad << 3D geometric structure of outdoor video scenes >> .


Leveraging [[ spatio-temporal video segmentation ]] , we decompose a << dynamic scene >> captured by a video into geometric classes , based on predictions made by region-classifiers that are trained on appearance and motion features .


Leveraging spatio-temporal video segmentation , we decompose a << dynamic scene >> captured by a video into [[ geometric classes ]] , based on predictions made by region-classifiers that are trained on appearance and motion features .


Leveraging spatio-temporal video segmentation , we decompose a dynamic scene captured by a video into << geometric classes >> , based on predictions made by [[ region-classifiers ]] that are trained on appearance and motion features .


Leveraging spatio-temporal video segmentation , we decompose a dynamic scene captured by a video into geometric classes , based on predictions made by << region-classifiers >> that are trained on [[ appearance and motion features ]] .


We built a novel , extensive [[ dataset ]] on geometric context of video to evaluate our << method >> , consisting of over 100 ground-truth annotated outdoor videos with over 20,000 frames .


We built a novel , extensive << dataset >> on [[ geometric context of video ]] to evaluate our method , consisting of over 100 ground-truth annotated outdoor videos with over 20,000 frames .


We built a novel , extensive << dataset >> on geometric context of video to evaluate our method , consisting of over 100 ground-truth [[ annotated outdoor videos ]] with over 20,000 frames .


To further scale beyond this dataset , we propose a [[ semi-supervised learning framework ]] to expand the pool of << labeled data >> with high confidence predictions obtained from unlabeled data .


To further scale beyond this dataset , we propose a << semi-supervised learning framework >> to expand the pool of labeled data with [[ high confidence predictions ]] obtained from unlabeled data .


To further scale beyond this dataset , we propose a semi-supervised learning framework to expand the pool of labeled data with << high confidence predictions >> obtained from [[ unlabeled data ]] .


Our [[ system ]] produces an accurate prediction of << geometric context of video >> achieving 96 % accuracy across main geometric classes .


Our << system >> produces an accurate prediction of geometric context of video achieving 96 % [[ accuracy ]] across main geometric classes .


This paper describes a [[ system ]] -LRB- RAREAS -RRB- which synthesizes << marine weather forecasts >> directly from formatted weather data .


This paper describes a << system >> -LRB- RAREAS -RRB- which synthesizes marine weather forecasts directly from [[ formatted weather data ]] .


Such << synthesis >> appears feasible in certain [[ natural sublanguages with stereotyped text structure ]] .


<< RAREAS >> draws on several kinds of [[ linguistic and non-linguistic knowledge ]] and mirrors a forecaster 's apparent tendency to ascribe less precise temporal adverbs to more remote meteorological events .


The << approach >> can easily be adapted to synthesize [[ bilingual or multi-lingual texts ]] .


We go , on to describe [[ FlexP ]] , a << bottom-up pattern-matching parser >> that we have designed and implemented to provide these flexibilities for restricted natural language input to a limited-domain computer system .


We go , on to describe FlexP , a [[ bottom-up pattern-matching parser ]] that we have designed and implemented to provide these << flexibilities >> for restricted natural language input to a limited-domain computer system .


We go , on to describe FlexP , a bottom-up pattern-matching parser that we have designed and implemented to provide these [[ flexibilities ]] for << restricted natural language >> input to a limited-domain computer system .


We go , on to describe FlexP , a bottom-up pattern-matching parser that we have designed and implemented to provide these [[ flexibilities ]] for restricted natural language input to a << limited-domain computer system >> .


We go , on to describe FlexP , a << bottom-up pattern-matching parser >> that we have designed and implemented to provide these flexibilities for [[ restricted natural language ]] input to a limited-domain computer system .


Traditional << information retrieval techniques >> use a [[ histogram of keywords ]] as the document representation but oral communication may offer additional indices such as the time and place of the rejoinder and the attendance .


Traditional information retrieval techniques use a [[ histogram of keywords ]] as the << document representation >> but oral communication may offer additional indices such as the time and place of the rejoinder and the attendance .


An alternative index could be the << activity >> such as [[ discussing ]] , planning , informing , story-telling , etc. .


An alternative index could be the activity such as [[ discussing ]] , << planning >> , informing , story-telling , etc. .


An alternative index could be the << activity >> such as discussing , [[ planning ]] , informing , story-telling , etc. .


An alternative index could be the activity such as discussing , [[ planning ]] , << informing >> , story-telling , etc. .


An alternative index could be the << activity >> such as discussing , planning , [[ informing ]] , story-telling , etc. .


An alternative index could be the activity such as discussing , planning , [[ informing ]] , << story-telling >> , etc. .


An alternative index could be the << activity >> such as discussing , planning , informing , [[ story-telling ]] , etc. .


This paper addresses the problem of the << automatic detection >> of those [[ activities ]] in meeting situation and everyday rejoinders .


The format of the << corpus >> adopts the [[ Child Language Data Exchange System -LRB- CHILDES -RRB- ]] .


In this paper , we describe [[ data collection ]] , << transcription >> , word segmentation , and part-of-speech annotation of this corpus .


In this paper , we describe [[ data collection ]] , transcription , word segmentation , and part-of-speech annotation of this << corpus >> .


In this paper , we describe data collection , [[ transcription ]] , << word segmentation >> , and part-of-speech annotation of this corpus .


In this paper , we describe data collection , [[ transcription ]] , word segmentation , and part-of-speech annotation of this << corpus >> .


In this paper , we describe data collection , transcription , [[ word segmentation ]] , and << part-of-speech annotation >> of this corpus .


In this paper , we describe data collection , transcription , [[ word segmentation ]] , and part-of-speech annotation of this << corpus >> .


In this paper , we describe data collection , transcription , word segmentation , and [[ part-of-speech annotation ]] of this << corpus >> .


This paper shows how << dictionary word sense definitions >> can be analysed by applying a [[ hierarchy of phrasal patterns ]] .


An experimental << system >> embodying this [[ mechanism ]] has been implemented for processing definitions from the Longman Dictionary of Contemporary English .


A property of this dictionary , exploited by the system , is that << it >> uses a [[ restricted vocabulary ]] in its word sense definitions .


A property of this dictionary , exploited by the system , is that it uses a [[ restricted vocabulary ]] in its << word sense definitions >> .


The structures generated by the experimental system are intended to be used for the << classification of new word senses >> in terms of the senses of words in the [[ restricted vocabulary ]] .


Thus the work reported addresses two [[ robustness problems ]] faced by current experimental << natural language processing systems >> : coping with an incomplete lexicon and with incomplete knowledge of phrasal constructions .


Thus the work reported addresses two << robustness problems >> faced by current experimental natural language processing systems : coping with an [[ incomplete lexicon ]] and with incomplete knowledge of phrasal constructions .


Thus the work reported addresses two << robustness problems >> faced by current experimental natural language processing systems : coping with an incomplete lexicon and with [[ incomplete knowledge of phrasal constructions ]] .


This paper presents a << word segmentation system >> in France Telecom R&D Beijing , which uses a unified [[ approach ]] to word breaking and OOV identification .


This paper presents a word segmentation system in France Telecom R&D Beijing , which uses a unified [[ approach ]] to << word breaking >> and OOV identification .


This paper presents a word segmentation system in France Telecom R&D Beijing , which uses a unified [[ approach ]] to word breaking and << OOV identification >> .


This paper presents a word segmentation system in France Telecom R&D Beijing , which uses a unified approach to [[ word breaking ]] and << OOV identification >> .


The system participated in all the tracks of the << segmentation bakeoff >> -- [[ PK-open ]] , PK-closed , AS-open , AS-closed , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .


The system participated in all the tracks of the segmentation bakeoff -- [[ PK-open ]] , << PK-closed >> , AS-open , AS-closed , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .


The system participated in all the tracks of the << segmentation bakeoff >> -- PK-open , [[ PK-closed ]] , AS-open , AS-closed , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .


The system participated in all the tracks of the segmentation bakeoff -- PK-open , [[ PK-closed ]] , << AS-open >> , AS-closed , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .


The system participated in all the tracks of the << segmentation bakeoff >> -- PK-open , PK-closed , [[ AS-open ]] , AS-closed , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .


The system participated in all the tracks of the segmentation bakeoff -- PK-open , PK-closed , [[ AS-open ]] , << AS-closed >> , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .


The system participated in all the tracks of the << segmentation bakeoff >> -- PK-open , PK-closed , AS-open , [[ AS-closed ]] , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .


The system participated in all the tracks of the segmentation bakeoff -- PK-open , PK-closed , AS-open , [[ AS-closed ]] , << HK-open >> , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .


The system participated in all the tracks of the << segmentation bakeoff >> -- PK-open , PK-closed , AS-open , AS-closed , [[ HK-open ]] , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .


The system participated in all the tracks of the segmentation bakeoff -- PK-open , PK-closed , AS-open , AS-closed , [[ HK-open ]] , << HK-closed >> , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .


The system participated in all the tracks of the << segmentation bakeoff >> -- PK-open , PK-closed , AS-open , AS-closed , HK-open , [[ HK-closed ]] , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .


The system participated in all the tracks of the segmentation bakeoff -- PK-open , PK-closed , AS-open , AS-closed , HK-open , [[ HK-closed ]] , << MSR-open >> and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .


The system participated in all the tracks of the << segmentation bakeoff >> -- PK-open , PK-closed , AS-open , AS-closed , HK-open , HK-closed , [[ MSR-open ]] and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .


The system participated in all the tracks of the segmentation bakeoff -- PK-open , PK-closed , AS-open , AS-closed , HK-open , HK-closed , [[ MSR-open ]] and << MSR - closed >> -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .


The system participated in all the tracks of the << segmentation bakeoff >> -- PK-open , PK-closed , AS-open , AS-closed , HK-open , HK-closed , MSR-open and [[ MSR - closed ]] -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .


The system participated in all the tracks of the segmentation bakeoff -- PK-open , PK-closed , AS-open , AS-closed , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in [[ MSR-open ]] , << MSR-close >> and PK-open tracks .


The system participated in all the tracks of the segmentation bakeoff -- PK-open , PK-closed , AS-open , AS-closed , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , [[ MSR-close ]] and << PK-open >> tracks .


This paper describes a [[ method ]] of << interactively visualizing and directing the process of translating >> a sentence .


The [[ method ]] allows a user to explore a << model >> of syntax-based statistical machine translation -LRB- MT -RRB- , to understand the model 's strengths and weaknesses , and to compare it to other MT systems .


The method allows a user to explore a [[ model ]] of << syntax-based statistical machine translation -LRB- MT -RRB- >> , to understand the model 's strengths and weaknesses , and to compare it to other MT systems .


The method allows a user to explore a model of syntax-based statistical machine translation -LRB- MT -RRB- , to understand the model 's strengths and weaknesses , and to compare [[ it ]] to other << MT systems >> .


Using this [[ visualization method ]] , we can find and address conceptual and practical problems in an << MT system >> .


A [[ method ]] of << sense resolution >> is proposed that is based on WordNet , an on-line lexical database that incorporates semantic relations -LRB- synonymy , antonymy , hyponymy , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses .


A << method >> of sense resolution is proposed that is based on [[ WordNet ]] , an on-line lexical database that incorporates semantic relations -LRB- synonymy , antonymy , hyponymy , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses .


A method of sense resolution is proposed that is based on [[ WordNet ]] , an << on-line lexical database >> that incorporates semantic relations -LRB- synonymy , antonymy , hyponymy , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses .


A method of sense resolution is proposed that is based on << WordNet >> , an on-line lexical database that incorporates [[ semantic relations ]] -LRB- synonymy , antonymy , hyponymy , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses .


A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates << semantic relations >> -LRB- [[ synonymy ]] , antonymy , hyponymy , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses .


A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates semantic relations -LRB- [[ synonymy ]] , << antonymy >> , hyponymy , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses .


A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates << semantic relations >> -LRB- synonymy , [[ antonymy ]] , hyponymy , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses .


A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates semantic relations -LRB- synonymy , [[ antonymy ]] , << hyponymy >> , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses .


A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates << semantic relations >> -LRB- synonymy , antonymy , [[ hyponymy ]] , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses .


A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates semantic relations -LRB- synonymy , antonymy , [[ hyponymy ]] , << meronymy >> , causal and troponymic entailment -RRB- as labeled pointers between word senses .


A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates << semantic relations >> -LRB- synonymy , antonymy , hyponymy , [[ meronymy ]] , causal and troponymic entailment -RRB- as labeled pointers between word senses .


A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates semantic relations -LRB- synonymy , antonymy , hyponymy , [[ meronymy ]] , << causal and troponymic entailment >> -RRB- as labeled pointers between word senses .


A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates << semantic relations >> -LRB- synonymy , antonymy , hyponymy , meronymy , [[ causal and troponymic entailment ]] -RRB- as labeled pointers between word senses .


With [[ WordNet ]] , it is easy to retrieve sets of << semantically related words >> , a facility that will be used for sense resolution during text processing , as follows .


With WordNet , it is easy to retrieve sets of [[ semantically related words ]] , a facility that will be used for << sense resolution >> during text processing , as follows .


With WordNet , it is easy to retrieve sets of semantically related words , a facility that will be used for [[ sense resolution ]] during << text processing >> , as follows .


Or , -LRB- 2 -RRB- the context of the polysemous word will be used as a key to search a large corpus ; all words found to occur in that context will be noted ; [[ WordNet ]] will then be used to estimate the << semantic distance >> from those words to the alternative senses of the polysemous word ; and that sense will be chosen that is closest in meaning to other words occurring in the same context If successful , this procedure could have practical applications to problems of information retrieval , mechanical translation , intelligent tutoring systems , and elsewhere .


Or , -LRB- 2 -RRB- the context of the polysemous word will be used as a key to search a large corpus ; all words found to occur in that context will be noted ; WordNet will then be used to estimate the semantic distance from those words to the alternative senses of the polysemous word ; and that sense will be chosen that is closest in meaning to other words occurring in the same context If successful , this [[ procedure ]] could have practical applications to problems of << information retrieval >> , mechanical translation , intelligent tutoring systems , and elsewhere .


Or , -LRB- 2 -RRB- the context of the polysemous word will be used as a key to search a large corpus ; all words found to occur in that context will be noted ; WordNet will then be used to estimate the semantic distance from those words to the alternative senses of the polysemous word ; and that sense will be chosen that is closest in meaning to other words occurring in the same context If successful , this [[ procedure ]] could have practical applications to problems of information retrieval , << mechanical translation >> , intelligent tutoring systems , and elsewhere .


Or , -LRB- 2 -RRB- the context of the polysemous word will be used as a key to search a large corpus ; all words found to occur in that context will be noted ; WordNet will then be used to estimate the semantic distance from those words to the alternative senses of the polysemous word ; and that sense will be chosen that is closest in meaning to other words occurring in the same context If successful , this [[ procedure ]] could have practical applications to problems of information retrieval , mechanical translation , << intelligent tutoring systems >> , and elsewhere .


Or , -LRB- 2 -RRB- the context of the polysemous word will be used as a key to search a large corpus ; all words found to occur in that context will be noted ; WordNet will then be used to estimate the semantic distance from those words to the alternative senses of the polysemous word ; and that sense will be chosen that is closest in meaning to other words occurring in the same context If successful , this procedure could have practical applications to problems of [[ information retrieval ]] , << mechanical translation >> , intelligent tutoring systems , and elsewhere .


Or , -LRB- 2 -RRB- the context of the polysemous word will be used as a key to search a large corpus ; all words found to occur in that context will be noted ; WordNet will then be used to estimate the semantic distance from those words to the alternative senses of the polysemous word ; and that sense will be chosen that is closest in meaning to other words occurring in the same context If successful , this procedure could have practical applications to problems of information retrieval , [[ mechanical translation ]] , << intelligent tutoring systems >> , and elsewhere .


The [[ interlingual approach ]] to << MT >> has been repeatedly advocated by researchers originally interested in natural language understanding who take machine translation to be one possible application .


In contrast , our project , the [[ Mu-project ]] , adopts the transfer approach as the basic framework of << MT >> .


In contrast , our project , the << Mu-project >> , adopts the [[ transfer approach ]] as the basic framework of MT .


This paper describes the detailed construction of the [[ transfer phase ]] of our << system >> from Japanese to English , and gives some examples of problems which seem difficult to treat in the interlingual approach .


The basic design principles of the [[ transfer phase ]] of our << system >> have already been mentioned in -LRB- 1 -RRB- -LRB- 2 -RRB- .


Some of the << principles >> which are relevant to the topic of this paper are : -LRB- a -RRB- [[ Multiple Layer of Grammars ]] -LRB- b -RRB- Multiple Layer Presentation -LRB- c -RRB- Lexicon Driven Processing -LRB- d -RRB- Form-Oriented Dictionary Description .


Some of the principles which are relevant to the topic of this paper are : -LRB- a -RRB- [[ Multiple Layer of Grammars ]] -LRB- b -RRB- << Multiple Layer Presentation >> -LRB- c -RRB- Lexicon Driven Processing -LRB- d -RRB- Form-Oriented Dictionary Description .


Some of the << principles >> which are relevant to the topic of this paper are : -LRB- a -RRB- Multiple Layer of Grammars -LRB- b -RRB- [[ Multiple Layer Presentation ]] -LRB- c -RRB- Lexicon Driven Processing -LRB- d -RRB- Form-Oriented Dictionary Description .


Some of the principles which are relevant to the topic of this paper are : -LRB- a -RRB- Multiple Layer of Grammars -LRB- b -RRB- [[ Multiple Layer Presentation ]] -LRB- c -RRB- << Lexicon Driven Processing >> -LRB- d -RRB- Form-Oriented Dictionary Description .


Some of the << principles >> which are relevant to the topic of this paper are : -LRB- a -RRB- Multiple Layer of Grammars -LRB- b -RRB- Multiple Layer Presentation -LRB- c -RRB- [[ Lexicon Driven Processing ]] -LRB- d -RRB- Form-Oriented Dictionary Description .


Some of the principles which are relevant to the topic of this paper are : -LRB- a -RRB- Multiple Layer of Grammars -LRB- b -RRB- Multiple Layer Presentation -LRB- c -RRB- [[ Lexicon Driven Processing ]] -LRB- d -RRB- << Form-Oriented Dictionary Description >> .


Some of the << principles >> which are relevant to the topic of this paper are : -LRB- a -RRB- Multiple Layer of Grammars -LRB- b -RRB- Multiple Layer Presentation -LRB- c -RRB- Lexicon Driven Processing -LRB- d -RRB- [[ Form-Oriented Dictionary Description ]] .


This paper also shows how these [[ principles ]] are realized in the current << system >> .


In this paper discourse segments are defined and a [[ method ]] for << discourse segmentation >> primarily based on abduction of temporal relations between segments is proposed .


In this paper discourse segments are defined and a method for << discourse segmentation >> primarily based on [[ abduction of temporal relations ]] between segments is proposed .


This << method >> is precise and computationally feasible and is supported by previous work in the area of [[ temporal anaphora resolution ]] .


This paper describes to what extent << deep processing >> may benefit from [[ shallow techniques ]] and it presents a NLP system which integrates a linguistic PoS tagger and chunker as a preprocessing module of a broad coverage unification based grammar of Spanish .


This paper describes to what extent deep processing may benefit from shallow techniques and it presents a NLP system which integrates a [[ linguistic PoS tagger and chunker ]] as a preprocessing module of a << broad coverage unification based grammar of Spanish >> .


This paper describes to what extent deep processing may benefit from shallow techniques and it presents a << NLP system >> which integrates a linguistic PoS tagger and chunker as a preprocessing module of a [[ broad coverage unification based grammar of Spanish ]] .


Experiments show that the efficiency of the overall analysis improves significantly and that our [[ system ]] also provides robustness to the << linguistic processing >> while maintaining both the accuracy and the precision of the grammar .


Experiments show that the efficiency of the overall analysis improves significantly and that our << system >> also provides [[ robustness ]] to the linguistic processing while maintaining both the accuracy and the precision of the grammar .


Experiments show that the efficiency of the overall analysis improves significantly and that our << system >> also provides robustness to the linguistic processing while maintaining both the [[ accuracy ]] and the precision of the grammar .


Experiments show that the efficiency of the overall analysis improves significantly and that our system also provides robustness to the linguistic processing while maintaining both the [[ accuracy ]] and the << precision >> of the grammar .


Experiments show that the efficiency of the overall analysis improves significantly and that our << system >> also provides robustness to the linguistic processing while maintaining both the accuracy and the [[ precision ]] of the grammar .


[[ Joint image filters ]] can leverage the guidance image as a prior and transfer the structural details from the guidance image to the target image for << suppressing noise >> or enhancing spatial resolution .


[[ Joint image filters ]] can leverage the guidance image as a prior and transfer the structural details from the guidance image to the target image for suppressing noise or << enhancing spatial resolution >> .


<< Joint image filters >> can leverage the [[ guidance image ]] as a prior and transfer the structural details from the guidance image to the target image for suppressing noise or enhancing spatial resolution .


Joint image filters can leverage the guidance image as a prior and transfer the structural details from the guidance image to the target image for [[ suppressing noise ]] or << enhancing spatial resolution >> .


<< Existing methods >> rely on various kinds of [[ explicit filter construction ]] or hand-designed objective functions .


Existing methods rely on various kinds of [[ explicit filter construction ]] or << hand-designed objective functions >> .


<< Existing methods >> rely on various kinds of explicit filter construction or [[ hand-designed objective functions ]] .


It is thus difficult to understand , improve , and accelerate << them >> in a [[ coherent framework ]] .


In this paper , we propose a [[ learning-based approach ]] to construct a << joint filter >> based on Convolution-al Neural Networks .


In this paper , we propose a << learning-based approach >> to construct a joint filter based on [[ Convolution-al Neural Networks ]] .


In contrast to existing [[ methods ]] that consider only the guidance image , our << method >> can selectively transfer salient structures that are consistent in both guidance and target images .


In contrast to existing << methods >> that consider only the [[ guidance image ]] , our method can selectively transfer salient structures that are consistent in both guidance and target images .


In contrast to existing methods that consider only the guidance image , our [[ method ]] can selectively << transfer salient structures >> that are consistent in both guidance and target images .


We show that the [[ model ]] trained on a certain type of data , e.g. , RGB and depth images , generalizes well for other << modalities >> , e.g. , Flash/Non-Flash and RGB/NIR images .


We show that the << model >> trained on a certain type of [[ data ]] , e.g. , RGB and depth images , generalizes well for other modalities , e.g. , Flash/Non-Flash and RGB/NIR images .


We show that the model trained on a certain type of << data >> , e.g. , [[ RGB and depth images ]] , generalizes well for other modalities , e.g. , Flash/Non-Flash and RGB/NIR images .


We show that the model trained on a certain type of data , e.g. , RGB and depth images , generalizes well for other << modalities >> , e.g. , [[ Flash/Non-Flash and RGB/NIR images ]] .


We validate the effectiveness of the proposed [[ joint filter ]] through extensive comparisons with << state-of-the-art methods >> .


In our current research into the design of << cognitively well-motivated interfaces >> relying primarily on the [[ display of graphical information ]] , we have observed that graphical information alone does not provide sufficient support to users - particularly when situations arise that do not simply conform to the users ' expectations .


To solve this problem , we are working towards the integration of [[ natural language generation ]] to augment the << interaction >>


A central problem of word sense disambiguation -LRB- WSD -RRB- is the lack of [[ manually sense-tagged data ]] required for << supervised learning >> .


In this paper , we evaluate an [[ approach ]] to automatically acquire << sense-tagged training data >> from English-Chinese parallel corpora , which are then used for disambiguating the nouns in the SENSEVAL-2 English lexical sample task .


In this paper , we evaluate an [[ approach ]] to automatically acquire sense-tagged training data from English-Chinese parallel corpora , which are then used for disambiguating the << nouns >> in the SENSEVAL-2 English lexical sample task .


In this paper , we evaluate an approach to automatically acquire << sense-tagged training data >> from [[ English-Chinese parallel corpora ]] , which are then used for disambiguating the nouns in the SENSEVAL-2 English lexical sample task .


In this paper , we evaluate an approach to automatically acquire sense-tagged training data from English-Chinese parallel corpora , which are then used for disambiguating the [[ nouns ]] in the << SENSEVAL-2 English lexical sample task >> .


Our investigation reveals that this [[ method ]] of << acquiring sense-tagged data >> is promising .


On a subset of the most difficult SENSEVAL-2 nouns , the accuracy difference between the two approaches is only 14.0 % , and the difference could narrow further to 6.5 % if we disregard the advantage that << manually sense-tagged data >> have in their [[ sense coverage ]] .


Our analysis also highlights the importance of the issue of [[ domain dependence ]] in << evaluating WSD programs >> .


This paper presents an analysis of << temporal anaphora >> in sentences which contain [[ quantification over events ]] , within the framework of Discourse Representation Theory .


This paper presents an analysis of << temporal anaphora >> in sentences which contain quantification over events , within the framework of [[ Discourse Representation Theory ]] .


The analysis in -LRB- Partee , 1984 -RRB- of quantified sentences , introduced by a temporal connective , gives the wrong truth-conditions when the [[ temporal connective ]] in the << subordinate clause >> is before or after .


This << problem >> has been previously analyzed in -LRB- de Swart , 1991 -RRB- as an instance of the proportion problem and given a solution from a [[ Generalized Quantifier approach ]] .


By using a careful distinction between the different notions of reference time based on -LRB- Kamp and Reyle , 1993 -RRB- , we propose a [[ solution ]] to this << problem >> , within the framework of DRT .


By using a careful distinction between the different notions of reference time based on -LRB- Kamp and Reyle , 1993 -RRB- , we propose a solution to this << problem >> , within the framework of [[ DRT ]] .


We show some applications of this [[ solution ]] to additional << temporal anaphora phenomena >> in quantified sentences .


We show some applications of this << solution >> to additional temporal anaphora phenomena in [[ quantified sentences ]] .


In this paper , we explore [[ correlation of dependency relation paths ]] to rank candidate answers in << answer extraction >> .


Using the [[ correlation measure ]] , we compare << dependency relations >> of a candidate answer and mapped question phrases in sentence with the corresponding relations in question .


Different from previous studies , we propose an approximate phrase mapping algorithm and incorporate the [[ mapping score ]] into the << correlation measure >> .


The [[ correlations ]] are further incorporated into a << Maximum Entropy-based ranking model >> which estimates path weights from training .


Experimental results show that our [[ method ]] significantly outperforms state-of-the-art << syntactic relation-based methods >> by up to 20 % in MRR .


Experimental results show that our << method >> significantly outperforms state-of-the-art syntactic relation-based methods by up to 20 % in [[ MRR ]] .


Experimental results show that our method significantly outperforms state-of-the-art << syntactic relation-based methods >> by up to 20 % in [[ MRR ]] .


[[ Evaluation ]] is also crucial to assessing competing claims and identifying promising technical << approaches >> .


Recently considerable progress has been made by a number of groups involved in the DARPA Spoken Language Systems -LRB- SLS -RRB- program to agree on a [[ methodology ]] for comparative << evaluation of SLS systems >> , and that methodology has been put into practice several times in comparative tests of several SLS systems .


Recently considerable progress has been made by a number of groups involved in the DARPA Spoken Language Systems -LRB- SLS -RRB- program to agree on a methodology for comparative evaluation of SLS systems , and that [[ methodology ]] has been put into practice several times in comparative tests of several << SLS systems >> .


These [[ evaluations ]] are probably the only << NL evaluations >> other than the series of Message Understanding Conferences -LRB- Sundheim , 1989 ; Sundheim , 1991 -RRB- to have been developed and used by a group of researchers at different sites , although several excellent workshops have been held to study some of these problems -LRB- Palmer et al. , 1989 ; Neal et al. , 1991 -RRB- .


These [[ evaluations ]] are probably the only NL evaluations other than the series of << Message Understanding Conferences >> -LRB- Sundheim , 1989 ; Sundheim , 1991 -RRB- to have been developed and used by a group of researchers at different sites , although several excellent workshops have been held to study some of these problems -LRB- Palmer et al. , 1989 ; Neal et al. , 1991 -RRB- .


This paper describes a practical [[ `` black-box '' methodology ]] for << automatic evaluation of question-answering NL systems >> .


While each new application domain will require some development of special resources , the heart of the methodology is domain-independent , and << it >> can be used with either [[ speech or text input ]] .


In this paper we present a novel [[ autonomous pipeline ]] to build a << personalized parametric model -LRB- pose-driven avatar -RRB- >> using a single depth sensor .


In this paper we present a novel << autonomous pipeline >> to build a personalized parametric model -LRB- pose-driven avatar -RRB- using a [[ single depth sensor ]] .


We fit each incomplete scan using << template fitting techniques >> with a generic [[ human template ]] , and register all scans to every pose using global consistency constraints .


After registration , these [[ watertight models ]] with different poses are used to train a << parametric model >> in a fashion similar to the SCAPE method .


After registration , these watertight models with different poses are used to train a << parametric model >> in a fashion similar to the [[ SCAPE method ]] .


Once the parametric model is built , [[ it ]] can be used as an << anim-itable avatar >> or more interestingly synthesizing dynamic 3D models from single-view depth videos .


Once the parametric model is built , [[ it ]] can be used as an anim-itable avatar or more interestingly synthesizing << dynamic 3D models >> from single-view depth videos .


Once the parametric model is built , it can be used as an anim-itable avatar or more interestingly synthesizing << dynamic 3D models >> from [[ single-view depth videos ]] .


Experimental results demonstrate the effectiveness of our [[ system ]] to produce << dynamic models >> .


In this paper , we propose a novel [[ algorithm ]] to detect/compensate << on-line interference effects >> when integrating Global Navigation Satellite System -LRB- GNSS -RRB- and Inertial Navigation System -LRB- INS -RRB- .


In this paper , we propose a novel algorithm to detect/compensate on-line interference effects when integrating [[ Global Navigation Satellite System -LRB- GNSS -RRB- ]] and << Inertial Navigation System -LRB- INS -RRB- >> .


The << GNSS/INS coupling >> is usually performed by an [[ Extended Kalman Filter -LRB- EKF -RRB- ]] which yields an accurate and robust localization .


The GNSS/INS coupling is usually performed by an [[ Extended Kalman Filter -LRB- EKF -RRB- ]] which yields an << accurate and robust localization >> .


We first study the impact of the GNSS noise inflation on the << covariance >> of the [[ EKF outputs ]] so as to compute a least square estimate of the potential variance jumps .


We first study the impact of the GNSS noise inflation on the covariance of the EKF outputs so as to compute a [[ least square estimate ]] of the potential << variance jumps >> .


Then , this [[ estimation ]] is used in a << Bayesian test >> which decides whether interference are corrupting the GNSS signal or not .


The results show the performance of the proposed << approach >> on [[ simulated data ]] .


We propose a [[ unified variational formulation ]] for << joint motion estimation and segmentation >> with explicit occlusion handling .


We propose a << unified variational formulation >> for joint motion estimation and segmentation with [[ explicit occlusion handling ]] .


We use a [[ convex formulation ]] of the << multi-label Potts model >> with label costs and show that the asymmetric map-uniqueness criterion can be integrated into our formulation by means of convex constraints .


We use a convex formulation of the multi-label Potts model with label costs and show that the [[ asymmetric map-uniqueness criterion ]] can be integrated into our << formulation >> by means of convex constraints .


We use a convex formulation of the multi-label Potts model with label costs and show that the asymmetric map-uniqueness criterion can be integrated into our << formulation >> by means of [[ convex constraints ]] .


By using a fast [[ primal-dual algorithm ]] we are able to handle several hundred << motion segments >> .


Two main classes of [[ approaches ]] have been studied to perform << monocular nonrigid 3D reconstruction >> : Template-based methods and Non-rigid Structure from Motion techniques .


Two main classes of << approaches >> have been studied to perform monocular nonrigid 3D reconstruction : [[ Template-based methods ]] and Non-rigid Structure from Motion techniques .


Two main classes of approaches have been studied to perform monocular nonrigid 3D reconstruction : [[ Template-based methods ]] and << Non-rigid Structure from Motion techniques >> .


Two main classes of << approaches >> have been studied to perform monocular nonrigid 3D reconstruction : Template-based methods and [[ Non-rigid Structure from Motion techniques ]] .


While the first [[ ones ]] have been applied to reconstruct << poorly-textured surfaces >> , they assume the availability of a 3D shape model prior to reconstruction .


While the first ones have been applied to reconstruct poorly-textured surfaces , << they >> assume the availability of a [[ 3D shape model ]] prior to reconstruction .


In this paper , we introduce a [[ template-free approach ]] to reconstructing a << poorly-textured , deformable surface >> .


To this end , we leverage [[ surface isometry ]] and formulate << 3D reconstruction >> as the joint problem of non-rigid image registration and depth estimation .


To this end , we leverage surface isometry and formulate << 3D reconstruction >> as the [[ joint problem of non-rigid image registration and depth estimation ]] .


Our experiments demonstrate that our [[ approach ]] yields much more accurate 3D reconstructions than << state-of-the-art techniques >> .


Our experiments demonstrate that our << approach >> yields much more accurate [[ 3D reconstructions ]] than state-of-the-art techniques .


Our experiments demonstrate that our approach yields much more accurate [[ 3D reconstructions ]] than << state-of-the-art techniques >> .


Many << computer vision applications >> , such as [[ image classification ]] and video indexing , are usually multi-label classification problems in which an instance can be assigned to more than one category .


Many computer vision applications , such as [[ image classification ]] and << video indexing >> , are usually multi-label classification problems in which an instance can be assigned to more than one category .


Many << computer vision applications >> , such as image classification and [[ video indexing ]] , are usually multi-label classification problems in which an instance can be assigned to more than one category .


Many << computer vision applications >> , such as image classification and video indexing , are usually [[ multi-label classification problems ]] in which an instance can be assigned to more than one category .


In this paper , we present a novel << multi-label classification approach >> with [[ hypergraph regu-larization ]] that addresses the correlations among different categories .


Then , an improved [[ SVM like learning system ]] incorporating the hypergraph regularization , called Rank-HLapSVM , is proposed to handle the << multi-label classification problems >> .


Then , an improved << SVM like learning system >> incorporating the [[ hypergraph regularization ]] , called Rank-HLapSVM , is proposed to handle the multi-label classification problems .


Then , an improved << SVM like learning system >> incorporating the hypergraph regularization , called [[ Rank-HLapSVM ]] , is proposed to handle the multi-label classification problems .


We find that the corresponding << optimization problem >> can be efficiently solved by the [[ dual coordinate descent method ]] .


Many promising experimental results on the [[ real datasets ]] including ImageCLEF and Me-diaMill demonstrate the effectiveness and efficiency of the proposed << algorithm >> .


Many promising experimental results on the << real datasets >> including [[ ImageCLEF ]] and Me-diaMill demonstrate the effectiveness and efficiency of the proposed algorithm .


Many promising experimental results on the real datasets including [[ ImageCLEF ]] and << Me-diaMill >> demonstrate the effectiveness and efficiency of the proposed algorithm .


Many promising experimental results on the << real datasets >> including ImageCLEF and [[ Me-diaMill ]] demonstrate the effectiveness and efficiency of the proposed algorithm .


We derive a [[ convex optimization problem ]] for the task of << segmenting sequential data >> , which explicitly treats presence of outliers .


We derive a convex optimization problem for the task of [[ segmenting sequential data ]] , which explicitly treats presence of << outliers >> .


We describe two [[ algorithms ]] for solving this << problem >> , one exact and one a top-down novel approach , and we derive a consistency results for the case of two segments and no outliers .


<< Robustness >> to [[ outliers ]] is evaluated on two real-world tasks related to speech segmentation .


<< Robustness >> to outliers is evaluated on two [[ real-world tasks ]] related to speech segmentation .


<< Robustness >> to outliers is evaluated on two real-world tasks related to [[ speech segmentation ]] .


Robustness to outliers is evaluated on two << real-world tasks >> related to [[ speech segmentation ]] .


Our [[ algorithms ]] outperform << baseline seg-mentation algorithms >> .


This paper examines the properties of << feature-based partial descriptions >> built on top of [[ Halliday 's systemic networks ]] .


We show that the crucial operation of [[ consistency checking ]] for such << descriptions >> is NP-complete , and therefore probably intractable , but proceed to develop algorithms which can sometimes alleviate the unpleasant consequences of this intractability .


We describe [[ Yoopick ]] , a << combinatorial sports prediction market >> that implements a flexible betting language , and in turn facilitates fine-grained probabilistic estimation of outcomes .


We describe [[ Yoopick ]] , a combinatorial sports prediction market that implements a flexible betting language , and in turn facilitates << fine-grained probabilistic estimation of outcomes >> .


We describe << Yoopick >> , a combinatorial sports prediction market that implements a [[ flexible betting language ]] , and in turn facilitates fine-grained probabilistic estimation of outcomes .


The goal of this paper is to discover a set of [[ discriminative patches ]] which can serve as a fully << unsupervised mid-level visual representation >> .


We pose this as an << unsupervised discriminative clustering problem >> on a huge dataset of [[ image patches ]] .


We use an iterative procedure which alternates between clustering and training discriminative classifiers , while applying careful [[ cross-validation ]] at each step to prevent << overfitting >> .


The paper experimentally demonstrates the effectiveness of [[ discriminative patches ]] as an << unsupervised mid-level visual representation >> , suggesting that it could be used in place of visual words for many tasks .


The paper experimentally demonstrates the effectiveness of << discriminative patches >> as an unsupervised mid-level visual representation , suggesting that [[ it ]] could be used in place of visual words for many tasks .


Furthermore , [[ discrim-inative patches ]] can also be used in a << supervised regime >> , such as scene classification , where they demonstrate state-of-the-art performance on the MIT Indoor-67 dataset .


Furthermore , discrim-inative patches can also be used in a << supervised regime >> , such as [[ scene classification ]] , where they demonstrate state-of-the-art performance on the MIT Indoor-67 dataset .


Furthermore , discrim-inative patches can also be used in a supervised regime , such as scene classification , where << they >> demonstrate state-of-the-art performance on the [[ MIT Indoor-67 dataset ]] .


We investigate the utility of an [[ algorithm ]] for << translation lexicon acquisition -LRB- SABLE -RRB- >> , used previously on a very large corpus to acquire general translation lexicons , when that algorithm is applied to a much smaller corpus to produce candidates for domain-specific translation lexicons .


We investigate the utility of an [[ algorithm ]] for translation lexicon acquisition -LRB- SABLE -RRB- , used previously on a very large corpus to acquire << general translation lexicons >> , when that algorithm is applied to a much smaller corpus to produce candidates for domain-specific translation lexicons .


We investigate the utility of an algorithm for translation lexicon acquisition -LRB- SABLE -RRB- , used previously on a very large corpus to acquire general translation lexicons , when that [[ algorithm ]] is applied to a much smaller corpus to produce candidates for << domain-specific translation lexicons >> .


This paper describes a [[ computational model ]] of << word segmentation >> and presents simulation results on realistic acquisition .


This paper describes a [[ computational model ]] of word segmentation and presents simulation results on << realistic acquisition >> .


In particular , we explore the capacity and limitations of << statistical learning mechanisms >> that have recently gained prominence in [[ cognitive psychology ]] and linguistics .


In particular , we explore the capacity and limitations of statistical learning mechanisms that have recently gained prominence in [[ cognitive psychology ]] and << linguistics >> .


In particular , we explore the capacity and limitations of << statistical learning mechanisms >> that have recently gained prominence in cognitive psychology and [[ linguistics ]] .


In the [[ model-based policy search approach ]] to << reinforcement learning -LRB- RL -RRB- >> , policies are found using a model -LRB- or `` simulator '' -RRB- of the Markov decision process .


In the model-based policy search approach to reinforcement learning -LRB- RL -RRB- , << policies >> are found using a model -LRB- or `` simulator '' -RRB- of the [[ Markov decision process ]] .


However , for << high-dimensional continuous-state tasks >> , it can be extremely difficult to build an accurate [[ model ]] , and thus often the algorithm returns a policy that works in simulation but not in real-life .


However , for high-dimensional continuous-state tasks , it can be extremely difficult to build an accurate model , and thus often the [[ algorithm ]] returns a << policy >> that works in simulation but not in real-life .


The other extreme , << model-free RL >> , tends to require infeasibly large numbers of [[ real-life trials ]] .


In this paper , we present a << hybrid algorithm >> that requires only an [[ approximate model ]] , and only a small number of real-life trials .


In this paper , we present a hybrid algorithm that requires only an << approximate model >> , and only a small number of [[ real-life trials ]] .


The key idea is to successively `` ground '' the << policy evaluations >> using [[ real-life trials ]] , but to rely on the approximate model to suggest local changes .


Empirical results also demonstrate that -- when given only a [[ crude model ]] and a small number of << real-life trials >> -- our algorithm can obtain near-optimal performance in the real system .


Empirical results also demonstrate that -- when given only a [[ crude model ]] and a small number of real-life trials -- our << algorithm >> can obtain near-optimal performance in the real system .


Empirical results also demonstrate that -- when given only a crude model and a small number of [[ real-life trials ]] -- our << algorithm >> can obtain near-optimal performance in the real system .


Although every << natural language system >> needs a [[ computational lexicon ]] , each system puts different amounts and types of information into its lexicon according to its individual needs .


This paper presents our experience in planning and building [[ COMPLEX ]] , a << computational lexicon >> designed to be a repository of shared lexical information for use by Natural Language Processing -LRB- NLP -RRB- systems .


This paper presents our experience in planning and building [[ COMPLEX ]] , a computational lexicon designed to be a repository of shared lexical information for use by << Natural Language Processing -LRB- NLP -RRB- systems >> .


Sentence planning is a set of inter-related but distinct << tasks >> , one of which is [[ sentence scoping ]] , i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences .


Sentence planning is a set of inter-related but distinct tasks , one of which is sentence scoping , i.e. the choice of [[ syntactic structure ]] for elementary << speech acts >> and the decision of how to combine them into one or more sentences .


In this paper , we present [[ SPoT ]] , a << sentence planner >> , and a new methodology for automatically training SPoT on the basis of feedback provided by human judges .


In this paper , we present SPoT , a sentence planner , and a new [[ methodology ]] for automatically training << SPoT >> on the basis of feedback provided by human judges .


First , a very simple , [[ randomized sentence-plan-generator -LRB- SPG -RRB- ]] generates a potentially large list of possible << sentence plans >> for a given text-plan input .


First , a very simple , << randomized sentence-plan-generator -LRB- SPG -RRB- >> generates a potentially large list of possible sentence plans for a given [[ text-plan input ]] .


Second , the [[ sentence-plan-ranker -LRB- SPR -RRB- ]] ranks the list of output << sentence plans >> , and then selects the top-ranked plan .


The << SPR >> uses [[ ranking rules ]] automatically learned from training data .


We show that the trained [[ SPR ]] learns to select a << sentence plan >> whose rating on average is only 5 % worse than the top human-ranked sentence plan .


We show that the trained SPR learns to select a [[ sentence plan ]] whose rating on average is only 5 % worse than the << top human-ranked sentence plan >> .


We discuss [[ maximum a posteriori estimation ]] of << continuous density hidden Markov models -LRB- CDHMM -RRB- >> .


The classical << MLE reestimation algorithms >> , namely the [[ forward-backward algorithm ]] and the segmental k-means algorithm , are expanded and reestimation formulas are given for HMM with Gaussian mixture observation densities .


The classical << MLE reestimation algorithms >> , namely the forward-backward algorithm and the [[ segmental k-means algorithm ]] , are expanded and reestimation formulas are given for HMM with Gaussian mixture observation densities .


The classical MLE reestimation algorithms , namely the << forward-backward algorithm >> and the [[ segmental k-means algorithm ]] , are expanded and reestimation formulas are given for HMM with Gaussian mixture observation densities .


The classical MLE reestimation algorithms , namely the forward-backward algorithm and the segmental k-means algorithm , are expanded and [[ reestimation formulas ]] are given for << HMM with Gaussian mixture observation densities >> .


Because of its adaptive nature , [[ Bayesian learning ]] serves as a unified approach for the following four << speech recognition applications >> , namely parameter smoothing , speaker adaptation , speaker group modeling and corrective training .


Because of its adaptive nature , Bayesian learning serves as a unified approach for the following four << speech recognition applications >> , namely [[ parameter smoothing ]] , speaker adaptation , speaker group modeling and corrective training .


Because of its adaptive nature , Bayesian learning serves as a unified approach for the following four speech recognition applications , namely [[ parameter smoothing ]] , << speaker adaptation >> , speaker group modeling and corrective training .


Because of its adaptive nature , Bayesian learning serves as a unified approach for the following four << speech recognition applications >> , namely parameter smoothing , [[ speaker adaptation ]] , speaker group modeling and corrective training .


Because of its adaptive nature , Bayesian learning serves as a unified approach for the following four speech recognition applications , namely parameter smoothing , [[ speaker adaptation ]] , << speaker group modeling >> and corrective training .


Because of its adaptive nature , Bayesian learning serves as a unified approach for the following four << speech recognition applications >> , namely parameter smoothing , speaker adaptation , [[ speaker group modeling ]] and corrective training .


Because of its adaptive nature , Bayesian learning serves as a unified approach for the following four speech recognition applications , namely parameter smoothing , speaker adaptation , [[ speaker group modeling ]] and << corrective training >> .


Because of its adaptive nature , Bayesian learning serves as a unified approach for the following four << speech recognition applications >> , namely parameter smoothing , speaker adaptation , speaker group modeling and [[ corrective training ]] .


New experimental results on all four [[ applications ]] are provided to show the effectiveness of the << MAP estimation approach >> .


This paper describes a characters-based Chinese collocation system and discusses the advantages of [[ it ]] over a traditional << word-based system >> .


Since wordbreaks are not conventionally marked in Chinese text corpora , a << character-based collocation system >> has the dual advantages of [[ avoiding pre-processing distortion ]] and directly accessing sub-lexical information .


Since wordbreaks are not conventionally marked in Chinese text corpora , a character-based collocation system has the dual advantages of [[ avoiding pre-processing distortion ]] and directly << accessing sub-lexical information >> .


Since wordbreaks are not conventionally marked in Chinese text corpora , a << character-based collocation system >> has the dual advantages of avoiding pre-processing distortion and directly [[ accessing sub-lexical information ]] .


Furthermore , << word-based collocational properties >> can be obtained through an [[ auxiliary module of automatic segmentation ]] .


This paper describes a [[ method ]] for << utterance classification >> that does not require manual transcription of training data .


The [[ method ]] combines domain independent acoustic models with off-the-shelf classifiers to give << utterance classification >> performance that is surprisingly close to what can be achieved using conventional word-trigram recognition requiring manual transcription .


The << method >> combines [[ domain independent acoustic models ]] with off-the-shelf classifiers to give utterance classification performance that is surprisingly close to what can be achieved using conventional word-trigram recognition requiring manual transcription .


The << method >> combines domain independent acoustic models with off-the-shelf [[ classifiers ]] to give utterance classification performance that is surprisingly close to what can be achieved using conventional word-trigram recognition requiring manual transcription .


The method combines << domain independent acoustic models >> with off-the-shelf [[ classifiers ]] to give utterance classification performance that is surprisingly close to what can be achieved using conventional word-trigram recognition requiring manual transcription .


The << method >> combines domain independent acoustic models with off-the-shelf classifiers to give utterance classification performance that is surprisingly close to what can be achieved using conventional [[ word-trigram recognition ]] requiring manual transcription .


The method combines domain independent acoustic models with off-the-shelf classifiers to give utterance classification performance that is surprisingly close to what can be achieved using conventional << word-trigram recognition >> requiring [[ manual transcription ]] .


In our << method >> , [[ unsupervised training ]] is first used to train a phone n-gram model for a particular domain ; the output of recognition with this model is then passed to a phone-string classifier .


In our method , [[ unsupervised training ]] is first used to train a << phone n-gram model >> for a particular domain ; the output of recognition with this model is then passed to a phone-string classifier .


In our method , [[ unsupervised training ]] is first used to train a phone n-gram model for a particular << domain >> ; the output of recognition with this model is then passed to a phone-string classifier .


The [[ classification accuracy ]] of the << method >> is evaluated on three different spoken language system domains .


The classification accuracy of the << method >> is evaluated on three different [[ spoken language system domains ]] .


The [[ Interval Algebra -LRB- IA -RRB- ]] and a subset of the << Region Connection Calculus -LRB- RCC -RRB- >> , namely RCC-8 , are the dominant Artificial Intelligence approaches for representing and reasoning about qualitative temporal and topological relations respectively .


The [[ Interval Algebra -LRB- IA -RRB- ]] and a subset of the Region Connection Calculus -LRB- RCC -RRB- , namely RCC-8 , are the dominant << Artificial Intelligence approaches >> for representing and reasoning about qualitative temporal and topological relations respectively .


The Interval Algebra -LRB- IA -RRB- and a subset of the [[ Region Connection Calculus -LRB- RCC -RRB- ]] , namely RCC-8 , are the dominant << Artificial Intelligence approaches >> for representing and reasoning about qualitative temporal and topological relations respectively .


The Interval Algebra -LRB- IA -RRB- and a subset of the << Region Connection Calculus -LRB- RCC -RRB- >> , namely [[ RCC-8 ]] , are the dominant Artificial Intelligence approaches for representing and reasoning about qualitative temporal and topological relations respectively .


The Interval Algebra -LRB- IA -RRB- and a subset of the Region Connection Calculus -LRB- RCC -RRB- , namely RCC-8 , are the dominant [[ Artificial Intelligence approaches ]] for << representing and reasoning about qualitative temporal and topological relations >> respectively .


Such << qualitative information >> can be formulated as a [[ Qualitative Constraint Network -LRB- QCN -RRB- ]] .


In this paper , we focus on the minimal labeling problem -LRB- MLP -RRB- and we propose an [[ algorithm ]] to efficiently derive all the feasible base relations of a << QCN >> .


Our << algorithm >> considers [[ chordal QCNs ]] and a new form of partial consistency which we define as ◆ G-consistency .


Our algorithm considers [[ chordal QCNs ]] and a new form of << partial consistency >> which we define as ◆ G-consistency .