The agreement in question involves number in [[ nouns ]] and << reflexive pronouns >> and is syntactic rather than semantic in nature because grammatical number in English , like grammatical gender in languages such as French , is partly arbitrary .


The agreement in question involves number in nouns and reflexive pronouns and is syntactic rather than semantic in nature because grammatical number in English , like [[ grammatical gender ]] in << languages >> such as French , is partly arbitrary .


The agreement in question involves number in nouns and reflexive pronouns and is syntactic rather than semantic in nature because grammatical number in English , like grammatical gender in << languages >> such as [[ French ]] , is partly arbitrary .


In this paper , a novel [[ method ]] to learn the << intrinsic object structure >> for robust visual tracking is proposed .


In this paper , a novel method to learn the [[ intrinsic object structure ]] for << robust visual tracking >> is proposed .


The basic assumption is that the << parameterized object state >> lies on a [[ low dimensional manifold ]] and can be learned from training data .


Based on this assumption , firstly we derived the [[ dimensionality reduction and density estimation algorithm ]] for << unsupervised learning of object intrinsic representation >> , the obtained non-rigid part of object state reduces even to 2 dimensions .


Secondly the << dynamical model >> is derived and trained based on this [[ intrinsic representation ]] .


Thirdly the learned [[ intrinsic object structure ]] is integrated into a << particle-filter style tracker >> .


We will show that this intrinsic object representation has some interesting properties and based on which the newly derived [[ dynamical model ]] makes << particle-filter style tracker >> more robust and reliable .


Experiments show that the learned [[ tracker ]] performs much better than existing << trackers >> on the tracking of complex non-rigid motions such as fish twisting with self-occlusion and large inter-frame lip motion .


Experiments show that the learned [[ tracker ]] performs much better than existing trackers on the << tracking of complex non-rigid motions >> such as fish twisting with self-occlusion and large inter-frame lip motion .


Experiments show that the learned tracker performs much better than existing [[ trackers ]] on the << tracking of complex non-rigid motions >> such as fish twisting with self-occlusion and large inter-frame lip motion .


Experiments show that the learned tracker performs much better than existing trackers on the tracking of << complex non-rigid motions >> such as [[ fish twisting ]] with self-occlusion and large inter-frame lip motion .


Experiments show that the learned tracker performs much better than existing trackers on the tracking of complex non-rigid motions such as << fish twisting >> with [[ self-occlusion ]] and large inter-frame lip motion .


Experiments show that the learned tracker performs much better than existing trackers on the tracking of complex non-rigid motions such as fish twisting with [[ self-occlusion ]] and large << inter-frame lip motion >> .


Experiments show that the learned tracker performs much better than existing trackers on the tracking of complex non-rigid motions such as << fish twisting >> with self-occlusion and large [[ inter-frame lip motion ]] .


The proposed [[ method ]] also has the potential to solve other type of << tracking problems >> .


In this paper , we present a [[ digital signal processor -LRB- DSP -RRB- implementation ]] of << real-time statistical voice conversion -LRB- VC -RRB- >> for silent speech enhancement and electrolaryngeal speech enhancement .


In this paper , we present a digital signal processor -LRB- DSP -RRB- implementation of [[ real-time statistical voice conversion -LRB- VC -RRB- ]] for << silent speech enhancement >> and electrolaryngeal speech enhancement .


In this paper , we present a digital signal processor -LRB- DSP -RRB- implementation of [[ real-time statistical voice conversion -LRB- VC -RRB- ]] for silent speech enhancement and << electrolaryngeal speech enhancement >> .


In this paper , we present a digital signal processor -LRB- DSP -RRB- implementation of real-time statistical voice conversion -LRB- VC -RRB- for [[ silent speech enhancement ]] and << electrolaryngeal speech enhancement >> .


[[ Electrolaryngeal speech ]] is one of the typical types of << alaryngeal speech >> produced by an alternative speaking method for laryngectomees .


Electrolaryngeal speech is one of the typical types of << alaryngeal speech >> produced by an alternative [[ speaking method ]] for laryngectomees .


Electrolaryngeal speech is one of the typical types of alaryngeal speech produced by an alternative [[ speaking method ]] for << laryngectomees >> .


However , the [[ sound quality ]] of << NAM and electrolaryngeal speech >> suffers from lack of naturalness .


VC has proven to be one of the promising approaches to address this problem , and << it >> has been successfully implemented on [[ devices ]] with sufficient computational resources .


VC has proven to be one of the promising approaches to address this problem , and it has been successfully implemented on << devices >> with [[ sufficient computational resources ]] .


An implementation on << devices >> that are highly portable but have [[ limited computational resources ]] would greatly contribute to its practical use .


In this paper we further implement << real-time VC >> on a [[ DSP ]] .


To implement the two << speech enhancement systems >> based on [[ real-time VC ]] , one from NAM to a whispered voice and the other from electrolaryngeal speech to a natural voice , we propose several methods for reducing computational cost while preserving conversion accuracy .


To implement the two << speech enhancement systems >> based on real-time VC , [[ one ]] from NAM to a whispered voice and the other from electrolaryngeal speech to a natural voice , we propose several methods for reducing computational cost while preserving conversion accuracy .


To implement the two speech enhancement systems based on real-time VC , [[ one ]] from NAM to a whispered voice and the << other >> from electrolaryngeal speech to a natural voice , we propose several methods for reducing computational cost while preserving conversion accuracy .


To implement the two << speech enhancement systems >> based on real-time VC , one from NAM to a whispered voice and the [[ other ]] from electrolaryngeal speech to a natural voice , we propose several methods for reducing computational cost while preserving conversion accuracy .


To implement the two speech enhancement systems based on real-time VC , one from NAM to a whispered voice and the other from electrolaryngeal speech to a natural voice , we propose several << methods >> for reducing [[ computational cost ]] while preserving conversion accuracy .


To implement the two speech enhancement systems based on real-time VC , one from NAM to a whispered voice and the other from electrolaryngeal speech to a natural voice , we propose several methods for reducing [[ computational cost ]] while preserving << conversion accuracy >> .


To implement the two speech enhancement systems based on real-time VC , one from NAM to a whispered voice and the other from electrolaryngeal speech to a natural voice , we propose several << methods >> for reducing computational cost while preserving [[ conversion accuracy ]] .


We conduct experimental evaluations and show that << real-time VC >> is capable of running on a [[ DSP ]] with little degradation .


We propose a [[ method ]] that automatically generates << paraphrase >> sets from seed sentences to be used as reference sets in objective machine translation evaluation measures like BLEU and NIST .


We propose a method that automatically generates [[ paraphrase ]] sets from seed sentences to be used as reference sets in objective << machine translation evaluation measures >> like BLEU and NIST .


We propose a method that automatically generates paraphrase sets from seed sentences to be used as reference sets in objective << machine translation evaluation measures >> like [[ BLEU ]] and NIST .


We propose a method that automatically generates paraphrase sets from seed sentences to be used as reference sets in objective machine translation evaluation measures like [[ BLEU ]] and << NIST >> .


We propose a method that automatically generates paraphrase sets from seed sentences to be used as reference sets in objective << machine translation evaluation measures >> like BLEU and [[ NIST ]] .


We measured the quality of the paraphrases produced in an experiment , i.e. , -LRB- i -RRB- their << grammaticality >> : at least 99 % correct sentences ; -LRB- ii -RRB- their [[ equivalence in meaning ]] : at least 96 % correct paraphrases either by meaning equivalence or entailment ; and , -LRB- iii -RRB- the amount of internal lexical and syntactical variation in a set of paraphrases : slightly superior to that of hand-produced sets .


We measured the quality of the paraphrases produced in an experiment , i.e. , -LRB- i -RRB- their grammaticality : at least 99 % correct sentences ; -LRB- ii -RRB- their equivalence in meaning : at least 96 % correct << paraphrases >> either by [[ meaning equivalence ]] or entailment ; and , -LRB- iii -RRB- the amount of internal lexical and syntactical variation in a set of paraphrases : slightly superior to that of hand-produced sets .


We measured the quality of the paraphrases produced in an experiment , i.e. , -LRB- i -RRB- their grammaticality : at least 99 % correct sentences ; -LRB- ii -RRB- their equivalence in meaning : at least 96 % correct paraphrases either by [[ meaning equivalence ]] or << entailment >> ; and , -LRB- iii -RRB- the amount of internal lexical and syntactical variation in a set of paraphrases : slightly superior to that of hand-produced sets .


We measured the quality of the paraphrases produced in an experiment , i.e. , -LRB- i -RRB- their grammaticality : at least 99 % correct sentences ; -LRB- ii -RRB- their equivalence in meaning : at least 96 % correct << paraphrases >> either by meaning equivalence or [[ entailment ]] ; and , -LRB- iii -RRB- the amount of internal lexical and syntactical variation in a set of paraphrases : slightly superior to that of hand-produced sets .


We measured the quality of the paraphrases produced in an experiment , i.e. , -LRB- i -RRB- their grammaticality : at least 99 % correct sentences ; -LRB- ii -RRB- their << equivalence in meaning >> : at least 96 % correct paraphrases either by meaning equivalence or entailment ; and , -LRB- iii -RRB- the amount of [[ internal lexical and syntactical variation ]] in a set of paraphrases : slightly superior to that of hand-produced sets .


We measured the quality of the paraphrases produced in an experiment , i.e. , -LRB- i -RRB- their grammaticality : at least 99 % correct sentences ; -LRB- ii -RRB- their equivalence in meaning : at least 96 % correct paraphrases either by meaning equivalence or entailment ; and , -LRB- iii -RRB- the amount of internal lexical and syntactical variation in a set of [[ paraphrases ]] : slightly superior to that of << hand-produced sets >> .


The << paraphrase >> sets produced by this [[ method ]] thus seem adequate as reference sets to be used for MT evaluation .


[[ Graph unification ]] remains the most expensive part of << unification-based grammar parsing >> .


We focus on one [[ speed-up element ]] in the design of << unification algorithms >> : avoidance of copying of unmodified subgraphs .


We propose a << method >> of attaining such a design through a method of [[ structure-sharing ]] which avoids log -LRB- d -RRB- overheads often associated with structure-sharing of graphs without any use of costly dependency pointers .


The proposed [[ scheme ]] eliminates redundant copying while maintaining the quasi-destructive scheme 's ability to avoid over copying and early copying combined with its ability to handle << cyclic structures >> without algorithmic additions .


The proposed << scheme >> eliminates redundant copying while maintaining the [[ quasi-destructive scheme 's ability ]] to avoid over copying and early copying combined with its ability to handle cyclic structures without algorithmic additions .


The proposed scheme eliminates redundant copying while maintaining the quasi-destructive scheme 's ability to avoid [[ over copying ]] and << early copying >> combined with its ability to handle cyclic structures without algorithmic additions .


We describe a novel technique and implemented [[ system ]] for constructing a << subcategorization dictionary >> from textual corpora .


We describe a novel technique and implemented << system >> for constructing a subcategorization dictionary from [[ textual corpora ]] .


We also demonstrate that a << subcategorization dictionary >> built with the [[ system ]] improves the accuracy of a parser by an appreciable amount


We also demonstrate that a subcategorization dictionary built with the system improves the [[ accuracy ]] of a << parser >> by an appreciable amount


We also demonstrate that a << subcategorization dictionary >> built with the system improves the accuracy of a [[ parser ]] by an appreciable amount


A number of powerful << registration criteria >> have been developed in the last decade , most prominently the criterion of [[ maximum mutual information ]] .


Although this criterion provides for good registration results in many applications , << it >> remains a purely [[ low-level criterion ]] .


In this paper , we will develop a [[ Bayesian framework ]] that allows to impose statistically learned prior knowledge about the joint intensity distribution into << image registration methods >> .


In this paper , we will develop a Bayesian framework that allows to impose [[ statistically learned prior knowledge ]] about the joint intensity distribution into << image registration methods >> .


In this paper , we will develop a Bayesian framework that allows to impose << statistically learned prior knowledge >> about the [[ joint intensity distribution ]] into image registration methods .


The << prior >> is given by a [[ kernel density estimate ]] on the space of joint intensity distributions computed from a representative set of pre-registered image pairs .


The prior is given by a [[ kernel density estimate ]] on the space of << joint intensity distributions >> computed from a representative set of pre-registered image pairs .


The prior is given by a kernel density estimate on the space of << joint intensity distributions >> computed from a representative set of [[ pre-registered image pairs ]] .


Experimental results demonstrate that the resulting [[ registration process ]] is more robust to << missing low-level information >> as it favors intensity correspondences statistically consistent with the learned intensity distributions .


Experimental results demonstrate that the resulting registration process is more robust to missing low-level information as [[ it ]] favors << intensity correspondences >> statistically consistent with the learned intensity distributions .


We present a [[ method ]] for << synthesizing complex , photo-realistic facade images >> , from a single example .


After parsing the example image into its << semantic components >> , a [[ tiling ]] for it is generated .


Novel tilings can then be created , yielding << facade textures >> with different dimensions or with [[ occluded parts inpainted ]] .


A [[ genetic algorithm ]] guides the novel << facades >> as well as inpainted parts to be consistent with the example , both in terms of their overall structure and their detailed textures .


A [[ genetic algorithm ]] guides the novel facades as well as << inpainted parts >> to be consistent with the example , both in terms of their overall structure and their detailed textures .


Promising results for [[ multiple standard datasets ]] -- in particular for the different building styles they contain -- demonstrate the potential of the << method >> .


We introduce a new << interactive corpus exploration tool >> called [[ InfoMagnets ]] .


[[ InfoMagnets ]] aims at making << exploratory corpus analysis >> accessible to researchers who are not experts in text mining .


As evidence of its usefulness and usability , [[ it ]] has been used successfully in a research context to uncover relationships between language and behavioral patterns in two distinct << domains >> : tutorial dialogue -LRB- Kumar et al. , submitted -RRB- and on-line communities -LRB- Arguello et al. , 2006 -RRB- .


As evidence of its usefulness and usability , it has been used successfully in a research context to uncover relationships between language and behavioral patterns in two distinct << domains >> : [[ tutorial dialogue ]] -LRB- Kumar et al. , submitted -RRB- and on-line communities -LRB- Arguello et al. , 2006 -RRB- .


As evidence of its usefulness and usability , it has been used successfully in a research context to uncover relationships between language and behavioral patterns in two distinct domains : [[ tutorial dialogue ]] -LRB- Kumar et al. , submitted -RRB- and << on-line communities >> -LRB- Arguello et al. , 2006 -RRB- .


As evidence of its usefulness and usability , it has been used successfully in a research context to uncover relationships between language and behavioral patterns in two distinct << domains >> : tutorial dialogue -LRB- Kumar et al. , submitted -RRB- and [[ on-line communities ]] -LRB- Arguello et al. , 2006 -RRB- .


As an [[ educational tool ]] , it has been used as part of a unit on << protocol analysis >> in an Educational Research Methods course .


Sources of training data suitable for << language modeling >> of [[ conversational speech ]] are limited .


In this paper , we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target << recognition task >> , but also that it is possible to get bigger performance gains from the data by using [[ class-dependent interpolation of N-grams ]] .


We present a [[ method ]] for << detecting 3D objects >> using multi-modalities .


We present a << method >> for detecting 3D objects using [[ multi-modalities ]] .


While [[ it ]] is generic , we demonstrate << it >> on the combination of an image and a dense depth map which give complementary object information .


While it is generic , we demonstrate << it >> on the combination of an [[ image ]] and a dense depth map which give complementary object information .


While it is generic , we demonstrate it on the combination of an [[ image ]] and a << dense depth map >> which give complementary object information .


While it is generic , we demonstrate << it >> on the combination of an image and a [[ dense depth map ]] which give complementary object information .


While it is generic , we demonstrate it on the combination of an image and a << dense depth map >> which give [[ complementary object information ]] .


It is based on an efficient representation of [[ templates ]] that capture the different << modalities >> , and we show in many experiments on commodity hardware that our approach significantly outperforms state-of-the-art methods on single modalities .


It is based on an efficient representation of templates that capture the different modalities , and we show in many experiments on commodity hardware that our [[ approach ]] significantly outperforms << state-of-the-art methods >> on single modalities .


It is based on an efficient representation of templates that capture the different modalities , and we show in many experiments on commodity hardware that our [[ approach ]] significantly outperforms state-of-the-art methods on << single modalities >> .


It is based on an efficient representation of templates that capture the different modalities , and we show in many experiments on commodity hardware that our approach significantly outperforms [[ state-of-the-art methods ]] on << single modalities >> .


The [[ compact description of a video sequence ]] through a single image map and a dominant motion has applications in several << domains >> , including video browsing and retrieval , compression , mosaicing , and visual summarization .


The << compact description of a video sequence >> through a single [[ image map ]] and a dominant motion has applications in several domains , including video browsing and retrieval , compression , mosaicing , and visual summarization .


The compact description of a video sequence through a single [[ image map ]] and a << dominant motion >> has applications in several domains , including video browsing and retrieval , compression , mosaicing , and visual summarization .


The << compact description of a video sequence >> through a single image map and a [[ dominant motion ]] has applications in several domains , including video browsing and retrieval , compression , mosaicing , and visual summarization .


The compact description of a video sequence through a single image map and a dominant motion has applications in several << domains >> , including [[ video browsing and retrieval ]] , compression , mosaicing , and visual summarization .


The compact description of a video sequence through a single image map and a dominant motion has applications in several domains , including [[ video browsing and retrieval ]] , << compression >> , mosaicing , and visual summarization .


The compact description of a video sequence through a single image map and a dominant motion has applications in several << domains >> , including video browsing and retrieval , [[ compression ]] , mosaicing , and visual summarization .


The compact description of a video sequence through a single image map and a dominant motion has applications in several domains , including video browsing and retrieval , [[ compression ]] , << mosaicing >> , and visual summarization .


The compact description of a video sequence through a single image map and a dominant motion has applications in several << domains >> , including video browsing and retrieval , compression , [[ mosaicing ]] , and visual summarization .


The compact description of a video sequence through a single image map and a dominant motion has applications in several domains , including video browsing and retrieval , compression , [[ mosaicing ]] , and << visual summarization >> .


Building such a representation requires the capability to register all the frames with respect to the dominant object in the scene , a << task >> which has been , in the past , addressed through temporally [[ localized motion estimates ]] .


To avoid this oscillation , we augment the << motion model >> with a [[ generic temporal constraint ]] which increases the robustness against competing interpretations , leading to more meaningful content summarization .


To avoid this oscillation , we augment the motion model with a [[ generic temporal constraint ]] which increases the robustness against competing interpretations , leading to more meaningful << content summarization >> .


To avoid this oscillation , we augment the motion model with a << generic temporal constraint >> which increases the [[ robustness ]] against competing interpretations , leading to more meaningful content summarization .


In cross-domain learning , there is a more challenging problem that the << domain divergence >> involves more than one [[ dominant factors ]] , e.g. , different viewpoints , various resolutions and changing illuminations .


In cross-domain learning , there is a more challenging problem that the domain divergence involves more than one << dominant factors >> , e.g. , different [[ viewpoints ]] , various resolutions and changing illuminations .


In cross-domain learning , there is a more challenging problem that the domain divergence involves more than one dominant factors , e.g. , different [[ viewpoints ]] , various << resolutions >> and changing illuminations .


In cross-domain learning , there is a more challenging problem that the domain divergence involves more than one << dominant factors >> , e.g. , different viewpoints , various [[ resolutions ]] and changing illuminations .


In cross-domain learning , there is a more challenging problem that the domain divergence involves more than one dominant factors , e.g. , different viewpoints , various [[ resolutions ]] and changing << illuminations >> .


Fortunately , an [[ intermediate domain ]] could often be found to build a bridge across them to facilitate the << learning problem >> .


In this paper , we propose a [[ Coupled Marginalized Denoising Auto-encoders framework ]] to address the << cross-domain problem >> .


Specifically , we design two << marginalized denoising auto-encoders >> , [[ one ]] for the target and the other for source as well as the intermediate one .


Specifically , we design two marginalized denoising auto-encoders , [[ one ]] for the target and the << other >> for source as well as the intermediate one .


Specifically , we design two << marginalized denoising auto-encoders >> , one for the target and the [[ other ]] for source as well as the intermediate one .


To better couple the two << denoising auto-encoders learning >> , we incorporate a [[ feature mapping ]] , which tends to transfer knowledge between the intermediate domain and the target one .


To better couple the two denoising auto-encoders learning , we incorporate a [[ feature mapping ]] , which tends to transfer knowledge between the << intermediate domain >> and the target one .


Furthermore , the << maximum margin criterion >> , e.g. , [[ intra-class com-pactness ]] and inter-class penalty , on the output layer is imposed to seek more discriminative features across different domains .


Furthermore , the maximum margin criterion , e.g. , [[ intra-class com-pactness ]] and << inter-class penalty >> , on the output layer is imposed to seek more discriminative features across different domains .


Furthermore , the << maximum margin criterion >> , e.g. , intra-class com-pactness and [[ inter-class penalty ]] , on the output layer is imposed to seek more discriminative features across different domains .


Extensive experiments on two [[ tasks ]] have demonstrated the superiority of our << method >> over the state-of-the-art methods .


Extensive experiments on two tasks have demonstrated the superiority of our [[ method ]] over the << state-of-the-art methods >> .


Basically , a set of << age-group specific dictionaries >> are learned , where the [[ dictionary bases ]] corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups , and a linear combination of these patterns expresses a particular personalized aging process .


Basically , a set of age-group specific dictionaries are learned , where the dictionary bases corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups , and a [[ linear combination ]] of these patterns expresses a particular << personalized aging process >> .


Basically , a set of age-group specific dictionaries are learned , where the dictionary bases corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups , and a << linear combination >> of these [[ patterns ]] expresses a particular personalized aging process .


First , beyond the aging dictionaries , each subject may have extra << personalized facial characteristics >> , e.g. [[ mole ]] , which are invariant in the aging process .


Thus a [[ personality-aware coupled reconstruction loss ]] is utilized to learn the << dictionaries >> based on face pairs from neighboring age groups .


Extensive experiments well demonstrate the advantages of our proposed [[ solution ]] over other << state-of-the-arts >> in term of personalized aging progression , as well as the performance gain for cross-age face verification by synthesizing aging faces .


Extensive experiments well demonstrate the advantages of our proposed [[ solution ]] over other state-of-the-arts in term of << personalized aging progression >> , as well as the performance gain for cross-age face verification by synthesizing aging faces .


Extensive experiments well demonstrate the advantages of our proposed solution over other [[ state-of-the-arts ]] in term of << personalized aging progression >> , as well as the performance gain for cross-age face verification by synthesizing aging faces .


Extensive experiments well demonstrate the advantages of our proposed solution over other state-of-the-arts in term of personalized aging progression , as well as the performance gain for << cross-age face verification >> by [[ synthesizing aging faces ]] .


We propose a draft scheme of the [[ model ]] formalizing the << structure of communicative context >> in dialogue interaction .


We propose a draft scheme of the model formalizing the << structure of communicative context >> in [[ dialogue interaction ]] .


Visitors who browse the web from wireless PDAs , cell phones , and pagers are frequently stymied by [[ web interfaces ]] optimized for << desktop PCs >> .


In this paper we develop an [[ algorithm ]] , MINPATH , that automatically improves << wireless web navigation >> by suggesting useful shortcut links in real time .


<< MINPATH >> finds shortcuts by using a learned [[ model ]] of web visitor behavior to estimate the savings of shortcut links , and suggests only the few best links .


MINPATH finds shortcuts by using a learned [[ model ]] of << web visitor behavior >> to estimate the savings of shortcut links , and suggests only the few best links .


MINPATH finds shortcuts by using a learned [[ model ]] of web visitor behavior to estimate the << savings of shortcut links >> , and suggests only the few best links .


We explore a variety of << predictive models >> , including [[ Na ¨ ıve Bayes mixture models ]] and mixtures of Markov models , and report empirical evidence that MINPATH finds useful shortcuts that save substantial navigational effort .


We explore a variety of predictive models , including [[ Na ¨ ıve Bayes mixture models ]] and << mixtures of Markov models >> , and report empirical evidence that MINPATH finds useful shortcuts that save substantial navigational effort .


We explore a variety of << predictive models >> , including Na ¨ ıve Bayes mixture models and [[ mixtures of Markov models ]] , and report empirical evidence that MINPATH finds useful shortcuts that save substantial navigational effort .


This paper describes a particular [[ approach ]] to << parsing >> that utilizes recent advances in unification-based parsing and in classification-based knowledge representation .


This paper describes a particular << approach >> to parsing that utilizes recent advances in [[ unification-based parsing ]] and in classification-based knowledge representation .


This paper describes a particular << approach >> to parsing that utilizes recent advances in unification-based parsing and in [[ classification-based knowledge representation ]] .


This paper describes a particular approach to parsing that utilizes recent advances in << unification-based parsing >> and in [[ classification-based knowledge representation ]] .


As [[ unification-based grammatical frameworks ]] are extended to handle richer descriptions of << linguistic information >> , they begin to share many of the properties that have been developed in KL-ONE-like knowledge representation systems .


As unification-based grammatical frameworks are extended to handle richer descriptions of linguistic information , << they >> begin to share many of the properties that have been developed in [[ KL-ONE-like knowledge representation systems ]] .


This commonality suggests that some of the [[ classification-based representation techniques ]] can be applied to << unification-based linguistic descriptions >> .


This merging supports the integration of [[ semantic and syntactic information ]] into the same << system >> , simultaneously subject to the same types of processes , in an efficient manner .


The use of a [[ KL-ONE style representation ]] for << parsing >> and semantic interpretation was first explored in the PSI-KLONE system -LSB- 2 -RSB- , in which parsing is characterized as an inference process called incremental description refinement .


The use of a [[ KL-ONE style representation ]] for parsing and << semantic interpretation >> was first explored in the PSI-KLONE system -LSB- 2 -RSB- , in which parsing is characterized as an inference process called incremental description refinement .


The use of a KL-ONE style representation for [[ parsing ]] and << semantic interpretation >> was first explored in the PSI-KLONE system -LSB- 2 -RSB- , in which parsing is characterized as an inference process called incremental description refinement .


The use of a << KL-ONE style representation >> for parsing and semantic interpretation was first explored in the [[ PSI-KLONE system ]] -LSB- 2 -RSB- , in which parsing is characterized as an inference process called incremental description refinement .


The use of a KL-ONE style representation for parsing and semantic interpretation was first explored in the PSI-KLONE system -LSB- 2 -RSB- , in which << parsing >> is characterized as an inference process called [[ incremental description refinement ]] .


The use of a KL-ONE style representation for parsing and semantic interpretation was first explored in the PSI-KLONE system -LSB- 2 -RSB- , in which parsing is characterized as an << inference process >> called [[ incremental description refinement ]] .


In this paper we discuss a proposed [[ user knowledge modeling architecture ]] for the << ICICLE system >> , a language tutoring application for deaf learners of written English .


In this paper we discuss a proposed user knowledge modeling architecture for the [[ ICICLE system ]] , a << language tutoring application >> for deaf learners of written English .


In this paper we discuss a proposed user knowledge modeling architecture for the ICICLE system , a [[ language tutoring application ]] for << deaf learners >> of written English .


In this paper we discuss a proposed user knowledge modeling architecture for the ICICLE system , a << language tutoring application >> for deaf learners of [[ written English ]] .


The [[ model ]] will represent the language proficiency of the user and is designed to be referenced during both << writing analysis >> and feedback production .


The [[ model ]] will represent the language proficiency of the user and is designed to be referenced during both writing analysis and << feedback production >> .


The model will represent the language proficiency of the user and is designed to be referenced during both [[ writing analysis ]] and << feedback production >> .


We motivate our << model design >> by citing relevant research on [[ second language and cognitive skill acquisition ]] , and briefly discuss preliminary empirical evidence supporting the design .


We conclude by showing how our [[ design ]] can provide a rich and robust information base to a << language assessment / correction application >> by modeling user proficiency at a high level of granularity and specificity .


We conclude by showing how our [[ design ]] can provide a rich and robust information base to a language assessment / correction application by modeling << user proficiency >> at a high level of granularity and specificity .


We conclude by showing how our design can provide a rich and robust information base to a language assessment / correction application by modeling << user proficiency >> at a high level of [[ granularity ]] and specificity .


We conclude by showing how our design can provide a rich and robust information base to a language assessment / correction application by modeling user proficiency at a high level of [[ granularity ]] and << specificity >> .


We conclude by showing how our design can provide a rich and robust information base to a language assessment / correction application by modeling << user proficiency >> at a high level of granularity and [[ specificity ]] .


[[ Constraint propagation ]] is one of the key techniques in << constraint programming >> , and a large body of work has built up around it .


In this paper we present << SHORTSTR2 >> , a development of the [[ Simple Tabular Reduction algorithm STR2 + ]] .


We show that [[ SHORTSTR2 ]] is complementary to the existing algorithms << SHORTGAC >> and HAGGISGAC that exploit short supports , while being much simpler .


We show that [[ SHORTSTR2 ]] is complementary to the existing algorithms SHORTGAC and << HAGGISGAC >> that exploit short supports , while being much simpler .


We show that SHORTSTR2 is complementary to the existing algorithms [[ SHORTGAC ]] and << HAGGISGAC >> that exploit short supports , while being much simpler .


When a constraint is amenable to short supports , the [[ short support set ]] can be exponentially smaller than the << full-length support set >> .


We also show that [[ SHORTSTR2 ]] can be combined with a simple algorithm to identify << short supports >> from full-length supports , to provide a superior drop-in replacement for STR2 + .


We also show that [[ SHORTSTR2 ]] can be combined with a simple algorithm to identify short supports from full-length supports , to provide a superior << drop-in replacement >> for STR2 + .


We also show that << SHORTSTR2 >> can be combined with a simple [[ algorithm ]] to identify short supports from full-length supports , to provide a superior drop-in replacement for STR2 + .


We also show that SHORTSTR2 can be combined with a simple [[ algorithm ]] to identify << short supports >> from full-length supports , to provide a superior drop-in replacement for STR2 + .


We also show that << SHORTSTR2 >> can be combined with a simple algorithm to identify short supports from [[ full-length supports ]] , to provide a superior drop-in replacement for STR2 + .


We also show that SHORTSTR2 can be combined with a simple << algorithm >> to identify short supports from [[ full-length supports ]] , to provide a superior drop-in replacement for STR2 + .


We also show that SHORTSTR2 can be combined with a simple algorithm to identify short supports from full-length supports , to provide a superior [[ drop-in replacement ]] for << STR2 + >> .


We propose a [[ detection method ]] for << orthographic variants >> caused by transliteration in a large corpus .


The << method >> employs two [[ similarities ]] .


One is << string similarity >> based on [[ edit distance ]] .


The other is << contextual similarity >> by a [[ vector space model ]] .


Experimental results show that the << method >> performed a 0.889 [[ F-measure ]] in an open test .


[[ Uncertainty handling ]] plays an important role during << shape tracking >> .


We have recently shown that the [[ fusion of measurement information with system dynamics and shape priors ]] greatly improves the << tracking >> performance for very noisy images such as ultrasound sequences -LSB- 22 -RSB- .


We have recently shown that the fusion of measurement information with system dynamics and shape priors greatly improves the [[ tracking ]] performance for very << noisy images >> such as ultrasound sequences -LSB- 22 -RSB- .


We have recently shown that the fusion of measurement information with system dynamics and shape priors greatly improves the tracking performance for very << noisy images >> such as [[ ultrasound sequences ]] -LSB- 22 -RSB- .


Nevertheless , this << approach >> required [[ user initialization ]] of the tracking process .


Nevertheless , this approach required [[ user initialization ]] of the << tracking process >> .


This paper solves the << automatic initial-ization problem >> by performing [[ boosted shape detection ]] as a generic measurement process and integrating it in our tracking framework .


This paper solves the automatic initial-ization problem by performing << boosted shape detection >> as a [[ generic measurement process ]] and integrating it in our tracking framework .


This paper solves the automatic initial-ization problem by performing boosted shape detection as a generic measurement process and integrating [[ it ]] in our << tracking framework >> .


As a result , we treat all sources of information in a unified way and derive the << posterior shape model >> as the shape with the [[ maximum likelihood ]] .


Our [[ framework ]] is applied for the << automatic tracking of endocardium >> in ultrasound sequences of the human heart .


Our framework is applied for the automatic tracking of [[ endocardium ]] in << ultrasound sequences of the human heart >> .


Reliable [[ detection ]] and robust << tracking >> results are achieved when compared to existing approaches and inter-expert variations .


Reliable detection and robust tracking results are achieved when compared to existing [[ approaches ]] and << inter-expert variations >> .


We present a [[ syntax-based constraint ]] for << word alignment >> , known as the cohesion constraint .


We present a << syntax-based constraint >> for word alignment , known as the [[ cohesion constraint ]] .


<< It >> requires disjoint [[ English phrases ]] to be mapped to non-overlapping intervals in the French sentence .


We evaluate the utility of this << constraint >> in two different [[ algorithms ]] .


The results show that << it >> can provide a significant improvement in [[ alignment quality ]] .


We present a novel << entity-based representation of discourse >> which is inspired by [[ Centering Theory ]] and can be computed automatically from raw text .


We present a novel << entity-based representation of discourse >> which is inspired by Centering Theory and can be computed automatically from [[ raw text ]] .


We view << coherence assessment >> as a [[ ranking learning problem ]] and show that the proposed discourse representation supports the effective learning of a ranking function .


We view coherence assessment as a ranking learning problem and show that the proposed [[ discourse representation ]] supports the effective learning of a << ranking function >> .


Our experiments demonstrate that the [[ induced model ]] achieves significantly higher accuracy than a state-of-the-art << coherence model >> .


Our experiments demonstrate that the << induced model >> achieves significantly higher [[ accuracy ]] than a state-of-the-art coherence model .


Our experiments demonstrate that the induced model achieves significantly higher [[ accuracy ]] than a state-of-the-art << coherence model >> .


This paper introduces a [[ robust interactive method ]] for << speech understanding >> .


The << generalized LR parsing >> is enhanced in this [[ approach ]] .


When a very noisy portion is detected , the << parser >> skips that portion using a fake [[ non-terminal symbol ]] .


This [[ method ]] is also capable of handling << unknown words >> , which is important in practical systems .


This paper shows that it is very often possible to identify the source language of [[ medium-length speeches ]] in the << EUROPARL corpus >> on the basis of frequency counts of word n-grams -LRB- 87.2 % -96.7 % accuracy depending on classification method -RRB- .


This paper shows that it is very often possible to identify the source language of medium-length speeches in the EUROPARL corpus on the basis of frequency counts of word n-grams -LRB- 87.2 % -96.7 % [[ accuracy ]] depending on << classification method >> -RRB- .


We investigated whether [[ automatic phonetic transcriptions -LRB- APTs -RRB- ]] can replace << manually verified phonetic transcriptions >> -LRB- MPTs -RRB- in a large corpus-based study on pronunciation variation .


We investigated whether [[ automatic phonetic transcriptions -LRB- APTs -RRB- ]] can replace manually verified phonetic transcriptions -LRB- MPTs -RRB- in a large corpus-based study on << pronunciation variation >> .


We investigated whether automatic phonetic transcriptions -LRB- APTs -RRB- can replace [[ manually verified phonetic transcriptions ]] -LRB- MPTs -RRB- in a large corpus-based study on << pronunciation variation >> .


We trained << classifiers >> on the [[ speech processes ]] extracted from the alignments of an APT and an MPT with a canonical transcription .


We trained classifiers on the << speech processes >> extracted from the [[ alignments ]] of an APT and an MPT with a canonical transcription .


We trained classifiers on the speech processes extracted from the [[ alignments ]] of an << APT >> and an MPT with a canonical transcription .


We trained classifiers on the speech processes extracted from the [[ alignments ]] of an APT and an << MPT >> with a canonical transcription .


We trained classifiers on the speech processes extracted from the alignments of an [[ APT ]] and an << MPT >> with a canonical transcription .


We trained classifiers on the speech processes extracted from the << alignments >> of an APT and an MPT with a [[ canonical transcription ]] .


We tested whether the [[ classifiers ]] were equally good at verifying whether << unknown transcriptions >> represent read speech or telephone dialogues , and whether the same speech processes were identified to distinguish between transcriptions of the two situational settings .


We tested whether the classifiers were equally good at verifying whether [[ unknown transcriptions ]] represent << read speech >> or telephone dialogues , and whether the same speech processes were identified to distinguish between transcriptions of the two situational settings .


We tested whether the classifiers were equally good at verifying whether [[ unknown transcriptions ]] represent read speech or << telephone dialogues >> , and whether the same speech processes were identified to distinguish between transcriptions of the two situational settings .


We tested whether the classifiers were equally good at verifying whether unknown transcriptions represent [[ read speech ]] or << telephone dialogues >> , and whether the same speech processes were identified to distinguish between transcriptions of the two situational settings .


Our results not only show that similar distinguishing speech processes were identified ; our [[ APT-based classifier ]] yielded better classification accuracy than the << MPT-based classifier >> whilst using fewer classification features .


Our results not only show that similar distinguishing speech processes were identified ; our << APT-based classifier >> yielded better [[ classification accuracy ]] than the MPT-based classifier whilst using fewer classification features .


Our results not only show that similar distinguishing speech processes were identified ; our APT-based classifier yielded better [[ classification accuracy ]] than the << MPT-based classifier >> whilst using fewer classification features .


Our results not only show that similar distinguishing speech processes were identified ; our << APT-based classifier >> yielded better classification accuracy than the MPT-based classifier whilst using fewer [[ classification features ]] .


Our results not only show that similar distinguishing speech processes were identified ; our APT-based classifier yielded better classification accuracy than the << MPT-based classifier >> whilst using fewer [[ classification features ]] .


Machine reading is a relatively new field that features [[ computer programs ]] designed to read << flowing text >> and extract fact assertions expressed by the narrative content .


Machine reading is a relatively new field that features [[ computer programs ]] designed to read flowing text and extract << fact assertions >> expressed by the narrative content .


Machine reading is a relatively new field that features computer programs designed to read flowing text and extract [[ fact assertions ]] expressed by the << narrative content >> .


This << task >> involves two core technologies : [[ natural language processing -LRB- NLP -RRB- ]] and information extraction -LRB- IE -RRB- .


This << task >> involves two core technologies : natural language processing -LRB- NLP -RRB- and [[ information extraction -LRB- IE -RRB- ]] .


In this paper we describe a << machine reading system >> that we have developed within a [[ cognitive architecture ]] .


We show how we have integrated into the framework several levels of knowledge for a particular domain , ideas from [[ cognitive semantics ]] and << construction grammar >> , plus tools from prior NLP and IE research .


We show how we have integrated into the framework several levels of knowledge for a particular domain , ideas from cognitive semantics and construction grammar , plus tools from [[ prior NLP ]] and << IE research >> .


The result is a [[ system ]] that is capable of reading and interpreting complex and fairly << idiosyncratic texts >> in the family history domain .


The result is a system that is capable of reading and interpreting complex and fairly << idiosyncratic texts >> in the [[ family history domain ]] .


We present two [[ methods ]] for capturing << nonstationary chaos >> , then present a few examples including biological signals , ocean waves and traffic flow .


We present two methods for capturing nonstationary chaos , then present a few << examples >> including [[ biological signals ]] , ocean waves and traffic flow .


We present two methods for capturing nonstationary chaos , then present a few examples including [[ biological signals ]] , << ocean waves >> and traffic flow .


We present two methods for capturing nonstationary chaos , then present a few << examples >> including biological signals , [[ ocean waves ]] and traffic flow .


We present two methods for capturing nonstationary chaos , then present a few examples including biological signals , [[ ocean waves ]] and << traffic flow >> .


We present two methods for capturing nonstationary chaos , then present a few << examples >> including biological signals , ocean waves and [[ traffic flow ]] .


This paper presents a [[ formal analysis ]] for a large class of words called << alternative markers >> , which includes other -LRB- than -RRB- , such -LRB- as -RRB- , and besides .


These [[ words ]] appear frequently enough in << dialog >> to warrant serious attention , yet present natural language search engines perform poorly on queries containing them .


I show that the performance of a << search engine >> can be improved dramatically by incorporating an [[ approximation of the formal analysis ]] that is compatible with the search engine 's operational semantics .


I show that the performance of a search engine can be improved dramatically by incorporating an approximation of the formal analysis that is compatible with the << search engine >> 's [[ operational semantics ]] .


The value of this approach is that as the [[ operational semantics ]] of << natural language applications >> improve , even larger improvements are possible .


We find that simple << interpolation methods >> , like [[ log-linear and linear interpolation ]] , improve the performance but fall short of the performance of an oracle .


Actually , the oracle acts like a << dynamic combiner >> with [[ hard decisions ]] using the reference .


We suggest a << method >> that mimics the behavior of the oracle using a [[ neural network ]] or a decision tree .


We suggest a << method >> that mimics the behavior of the oracle using a neural network or a [[ decision tree ]] .


We suggest a method that mimics the behavior of the oracle using a << neural network >> or a [[ decision tree ]] .


The [[ method ]] amounts to tagging << LMs >> with confidence measures and picking the best hypothesis corresponding to the LM with the best confidence .


The << method >> amounts to tagging LMs with [[ confidence measures ]] and picking the best hypothesis corresponding to the LM with the best confidence .


We describe a new [[ method ]] for the representation of << NLP structures >> within reranking approaches .


We describe a new method for the representation of << NLP structures >> within [[ reranking approaches ]] .


We make use of a << conditional log-linear model >> , with [[ hidden variables ]] representing the assignment of lexical items to word clusters or word senses .


We make use of a conditional log-linear model , with hidden variables representing the assignment of lexical items to [[ word clusters ]] or << word senses >> .


The << model >> learns to automatically make these assignments based on a [[ discriminative training criterion ]] .


Training and decoding with the model requires summing over an exponential number of hidden-variable assignments : the required << summations >> can be computed efficiently and exactly using [[ dynamic programming ]] .


As a case study , we apply the [[ model ]] to << parse reranking >> .


The [[ model ]] gives an F-measure improvement of ~ 1.25 % beyond the << base parser >> , and an ~ 0.25 % improvement beyond Collins -LRB- 2000 -RRB- reranker .


The << model >> gives an [[ F-measure ]] improvement of ~ 1.25 % beyond the base parser , and an ~ 0.25 % improvement beyond Collins -LRB- 2000 -RRB- reranker .


The model gives an F-measure improvement of ~ 1.25 % beyond the [[ base parser ]] , and an ~ 0.25 % improvement beyond << Collins -LRB- 2000 -RRB- reranker >> .


Although our experiments are focused on << parsing >> , the [[ techniques ]] described generalize naturally to NLP structures other than parse trees .


Although our experiments are focused on parsing , the [[ techniques ]] described generalize naturally to << NLP structures >> other than parse trees .


Although our experiments are focused on parsing , the [[ techniques ]] described generalize naturally to NLP structures other than << parse trees >> .


Although our experiments are focused on parsing , the techniques described generalize naturally to << NLP structures >> other than [[ parse trees ]] .


This paper presents an [[ algorithm ]] for << learning the time-varying shape of a non-rigid 3D object >> from uncalibrated 2D tracking data .


We constrain the problem by assuming that the << object shape >> at each time instant is drawn from a [[ Gaussian distribution ]] .


Based on this assumption , the [[ algorithm ]] simultaneously estimates << 3D shape and motion >> for each time frame , learns the parameters of the Gaussian , and robustly fills-in missing data points .


We then extend the [[ algorithm ]] to model << temporal smoothness in object shape >> , thus allowing it to handle severe cases of missing data .


We then extend the algorithm to model temporal smoothness in object shape , thus allowing [[ it ]] to handle severe cases of << missing data >> .


[[ Automatic summarization ]] and << information extraction >> are two important Internet services .


[[ MUC ]] and << SUMMAC >> play their appropriate roles in the next generation Internet .


This paper focuses on the automatic summarization and proposes two different [[ models ]] to extract sentences for << summary generation >> under two tasks initiated by SUMMAC-1 .


This paper focuses on the automatic summarization and proposes two different [[ models ]] to extract sentences for summary generation under two << tasks >> initiated by SUMMAC-1 .


This paper focuses on the automatic summarization and proposes two different models to extract sentences for summary generation under two [[ tasks ]] initiated by << SUMMAC-1 >> .


For << categorization task >> , [[ positive feature vectors ]] and negative feature vectors are used cooperatively to construct generic , indicative summaries .


For categorization task , [[ positive feature vectors ]] and << negative feature vectors >> are used cooperatively to construct generic , indicative summaries .


For categorization task , [[ positive feature vectors ]] and negative feature vectors are used cooperatively to construct << generic , indicative summaries >> .


For << categorization task >> , positive feature vectors and [[ negative feature vectors ]] are used cooperatively to construct generic , indicative summaries .


For categorization task , positive feature vectors and [[ negative feature vectors ]] are used cooperatively to construct << generic , indicative summaries >> .


For << adhoc task >> , a [[ text model ]] based on relationship between nouns and verbs is used to filter out irrelevant discourse segment , to rank relevant sentences , and to generate the user-directed summaries .


For adhoc task , a [[ text model ]] based on relationship between nouns and verbs is used to filter out irrelevant << discourse segment >> , to rank relevant sentences , and to generate the user-directed summaries .


For adhoc task , a [[ text model ]] based on relationship between nouns and verbs is used to filter out irrelevant discourse segment , to rank relevant sentences , and to generate the << user-directed summaries >> .


The result shows that the [[ NormF ]] of the best summary and that of the fixed summary for << adhoc tasks >> are 0.456 and 0 .


The [[ NormF ]] of the best summary and that of the fixed summary for << categorization task >> are 0.4090 and 0.4023 .


Our [[ system ]] outperforms the average << system >> in categorization task but does a common job in adhoc task .


Our << system >> outperforms the average system in [[ categorization task ]] but does a common job in adhoc task .


Our system outperforms the average << system >> in [[ categorization task ]] but does a common job in adhoc task .


Our << system >> outperforms the average system in categorization task but does a common job in [[ adhoc task ]] .


Our system outperforms the average system in << categorization task >> but does a common job in [[ adhoc task ]] .


In real-world action recognition problems , low-level features can not adequately characterize the [[ rich spatial-temporal structures ]] in << action videos >> .


The second type is << data-driven attributes >> , which are learned from data using [[ dictionary learning methods ]] .


We propose a << discriminative and compact attribute-based representation >> by selecting a subset of [[ discriminative attributes ]] from a large attribute set .


Three << attribute selection criteria >> are proposed and formulated as a [[ submodular optimization problem ]] .


Experimental results on the [[ Olympic Sports and UCF101 datasets ]] demonstrate that the proposed << attribute-based representation >> can significantly boost the performance of action recognition algorithms and outperform most recently proposed recognition approaches .


Experimental results on the Olympic Sports and UCF101 datasets demonstrate that the proposed [[ attribute-based representation ]] can significantly boost the performance of << action recognition algorithms >> and outperform most recently proposed recognition approaches .


Experimental results on the Olympic Sports and UCF101 datasets demonstrate that the proposed attribute-based representation can significantly boost the performance of [[ action recognition algorithms ]] and outperform most recently proposed << recognition approaches >> .


Landsbergen 's advocacy of [[ analytical inverses ]] for << compositional syntax rules >> encourages the application of Definite Clause Grammar techniques to the construction of a parser returning Montague analysis trees .


Landsbergen 's advocacy of [[ analytical inverses ]] for compositional syntax rules encourages the application of << Definite Clause Grammar techniques >> to the construction of a parser returning Montague analysis trees .


Landsbergen 's advocacy of analytical inverses for compositional syntax rules encourages the application of [[ Definite Clause Grammar techniques ]] to the construction of a << parser returning Montague analysis trees >> .


A << parser MDCC >> is presented which implements an [[ augmented Friedman - Warren algorithm ]] permitting post referencing * and interfaces with a language of intenslonal logic translator LILT so as to display the derivational history of corresponding reduced IL formulae .


A parser MDCC is presented which implements an << augmented Friedman - Warren algorithm >> permitting [[ post referencing ]] * and interfaces with a language of intenslonal logic translator LILT so as to display the derivational history of corresponding reduced IL formulae .


A parser MDCC is presented which implements an augmented Friedman - Warren algorithm permitting post referencing * and interfaces with a language of << intenslonal logic translator LILT >> so as to display the [[ derivational history ]] of corresponding reduced IL formulae .


A parser MDCC is presented which implements an augmented Friedman - Warren algorithm permitting post referencing * and interfaces with a language of intenslonal logic translator LILT so as to display the << derivational history >> of corresponding [[ reduced IL formulae ]] .


Some familiarity with [[ Montague 's PTQ ]] and the << basic DCG mechanism >> is assumed .


<< Stochastic attention-based models >> have been shown to improve [[ computational efficiency ]] at test time , but they remain difficult to train because of intractable posterior inference and high variance in the stochastic gradient estimates .


Stochastic attention-based models have been shown to improve computational efficiency at test time , but they remain difficult to train because of [[ intractable posterior inference ]] and high variance in the << stochastic gradient estimates >> .


[[ Borrowing techniques ]] from the literature on training << deep generative models >> , we present the Wake-Sleep Recurrent Attention Model , a method for training stochastic attention networks which improves posterior inference and which reduces the variability in the stochastic gradients .


Borrowing techniques from the literature on training deep generative models , we present the Wake-Sleep Recurrent Attention Model , a [[ method ]] for training << stochastic attention networks >> which improves posterior inference and which reduces the variability in the stochastic gradients .


Borrowing techniques from the literature on training deep generative models , we present the Wake-Sleep Recurrent Attention Model , a method for training [[ stochastic attention networks ]] which improves << posterior inference >> and which reduces the variability in the stochastic gradients .


We show that our << method >> can greatly speed up the [[ training time ]] for stochastic attention networks in the domains of image classification and caption generation .


We show that our method can greatly speed up the [[ training time ]] for << stochastic attention networks >> in the domains of image classification and caption generation .


We show that our << method >> can greatly speed up the training time for stochastic attention networks in the domains of [[ image classification ]] and caption generation .


We show that our method can greatly speed up the training time for stochastic attention networks in the domains of [[ image classification ]] and << caption generation >> .


We show that our << method >> can greatly speed up the training time for stochastic attention networks in the domains of image classification and [[ caption generation ]] .


A new [[ exemplar-based framework ]] unifying << image completion >> , texture synthesis and image inpainting is presented in this work .


A new [[ exemplar-based framework ]] unifying image completion , << texture synthesis >> and image inpainting is presented in this work .


A new [[ exemplar-based framework ]] unifying image completion , texture synthesis and << image inpainting >> is presented in this work .


A new exemplar-based framework unifying [[ image completion ]] , << texture synthesis >> and image inpainting is presented in this work .


A new exemplar-based framework unifying image completion , [[ texture synthesis ]] and << image inpainting >> is presented in this work .


Contrary to existing [[ greedy techniques ]] , these << tasks >> are posed in the form of a discrete global optimization problem with a well defined objective function .


Contrary to existing greedy techniques , these << tasks >> are posed in the form of a [[ discrete global optimization problem ]] with a well defined objective function .


Contrary to existing greedy techniques , these tasks are posed in the form of a << discrete global optimization problem >> with a [[ well defined objective function ]] .


For solving this << problem >> a novel [[ optimization scheme ]] , called Priority-BP , is proposed which carries two very important extensions over standard belief propagation -LRB- BP -RRB- : '' priority-based message scheduling '' and '' dynamic label pruning '' .


For solving this problem a novel << optimization scheme >> , called [[ Priority-BP ]] , is proposed which carries two very important extensions over standard belief propagation -LRB- BP -RRB- : '' priority-based message scheduling '' and '' dynamic label pruning '' .


For solving this problem a novel << optimization scheme >> , called Priority-BP , is proposed which carries two very important [[ extensions ]] over standard belief propagation -LRB- BP -RRB- : '' priority-based message scheduling '' and '' dynamic label pruning '' .


For solving this problem a novel optimization scheme , called Priority-BP , is proposed which carries two very important << extensions >> over standard [[ belief propagation -LRB- BP -RRB- ]] : '' priority-based message scheduling '' and '' dynamic label pruning '' .


For solving this problem a novel optimization scheme , called Priority-BP , is proposed which carries two very important << extensions >> over standard belief propagation -LRB- BP -RRB- : '' [[ priority-based message scheduling ]] '' and '' dynamic label pruning '' .


For solving this problem a novel optimization scheme , called Priority-BP , is proposed which carries two very important extensions over standard belief propagation -LRB- BP -RRB- : '' [[ priority-based message scheduling ]] '' and '' << dynamic label pruning >> '' .


For solving this problem a novel optimization scheme , called Priority-BP , is proposed which carries two very important << extensions >> over standard belief propagation -LRB- BP -RRB- : '' priority-based message scheduling '' and '' [[ dynamic label pruning ]] '' .


These two [[ extensions ]] work in cooperation to deal with the << intolerable computational cost of BP >> caused by the huge number of existing labels .


Moreover , both [[ extensions ]] are generic and can therefore be applied to any << MRF energy function >> as well .


The effectiveness of our << method >> is demonstrated on a wide variety of [[ image completion examples ]] .


In this paper , we compare the relative effects of [[ segment order ]] , << segmentation >> and segment contiguity on the retrieval performance of a translation memory system .


In this paper , we compare the relative effects of [[ segment order ]] , segmentation and segment contiguity on the retrieval performance of a << translation memory system >> .


In this paper , we compare the relative effects of segment order , [[ segmentation ]] and << segment contiguity >> on the retrieval performance of a translation memory system .


In this paper , we compare the relative effects of segment order , [[ segmentation ]] and segment contiguity on the retrieval performance of a << translation memory system >> .


In this paper , we compare the relative effects of segment order , segmentation and [[ segment contiguity ]] on the retrieval performance of a << translation memory system >> .


In this paper , we compare the relative effects of segment order , segmentation and segment contiguity on the [[ retrieval ]] performance of a << translation memory system >> .


We take a selection of both << bag-of-words and segment order-sensitive string comparison methods >> , and run each over both [[ character - and word-segmented data ]] , in combination with a range of local segment contiguity models -LRB- in the form of N-grams -RRB- .


We take a selection of both << bag-of-words and segment order-sensitive string comparison methods >> , and run each over both character - and word-segmented data , in combination with a range of [[ local segment contiguity models ]] -LRB- in the form of N-grams -RRB- .


We take a selection of both bag-of-words and segment order-sensitive string comparison methods , and run each over both character - and word-segmented data , in combination with a range of << local segment contiguity models >> -LRB- in the form of [[ N-grams ]] -RRB- .


Over two distinct datasets , we find that << indexing >> according to simple [[ character bigrams ]] produces a retrieval accuracy superior to any of the tested word N-gram models .


Over two distinct datasets , we find that indexing according to simple [[ character bigrams ]] produces a retrieval accuracy superior to any of the tested << word N-gram models >> .


Over two distinct datasets , we find that indexing according to simple << character bigrams >> produces a [[ retrieval accuracy ]] superior to any of the tested word N-gram models .


Over two distinct datasets , we find that indexing according to simple character bigrams produces a [[ retrieval accuracy ]] superior to any of the tested << word N-gram models >> .


Further , in their optimum configuration , [[ bag-of-words methods ]] are shown to be equivalent to << segment order-sensitive methods >> in terms of retrieval accuracy , but much faster .


Further , in their optimum configuration , << bag-of-words methods >> are shown to be equivalent to segment order-sensitive methods in terms of [[ retrieval accuracy ]] , but much faster .


Further , in their optimum configuration , bag-of-words methods are shown to be equivalent to << segment order-sensitive methods >> in terms of [[ retrieval accuracy ]] , but much faster .


In this paper we show how two standard [[ outputs ]] from information extraction -LRB- IE -RRB- systems - named entity annotations and scenario templates - can be used to enhance access to << text collections >> via a standard text browser .


In this paper we show how two standard << outputs >> from information extraction -LRB- IE -RRB- systems - [[ named entity annotations ]] and scenario templates - can be used to enhance access to text collections via a standard text browser .


In this paper we show how two standard outputs from information extraction -LRB- IE -RRB- systems - [[ named entity annotations ]] and << scenario templates >> - can be used to enhance access to text collections via a standard text browser .


In this paper we show how two standard << outputs >> from information extraction -LRB- IE -RRB- systems - named entity annotations and [[ scenario templates ]] - can be used to enhance access to text collections via a standard text browser .


In this paper we show how two standard outputs from information extraction -LRB- IE -RRB- systems - named entity annotations and scenario templates - can be used to enhance access to << text collections >> via a standard [[ text browser ]] .


We describe how this information is used in a [[ prototype system ]] designed to support information workers ' access to a << pharmaceutical news archive >> as part of their industry watch function .


We also report results of a preliminary , [[ qualitative user evaluation ]] of the << system >> , which while broadly positive indicates further work needs to be done on the interface to make users aware of the increased potential of IE-enhanced text browsers .


We present a new [[ model-based bundle adjustment algorithm ]] to recover the << 3D model >> of a scene/object from a sequence of images with unknown motions .


We present a new model-based bundle adjustment algorithm to recover the << 3D model >> of a scene/object from a sequence of [[ images ]] with unknown motions .


We present a new model-based bundle adjustment algorithm to recover the 3D model of a scene/object from a sequence of << images >> with [[ unknown motions ]] .


Instead of representing scene/object by a collection of isolated 3D features -LRB- usually points -RRB- , our << algorithm >> uses a [[ surface ]] controlled by a small set of parameters .


Compared with previous [[ model-based approaches ]] , our << approach >> has the following advantages .


First , instead of using the [[ model space ]] as a << regular-izer >> , we directly use it as our search space , thus resulting in a more elegant formulation with fewer unknowns and fewer equations .


First , instead of using the model space as a [[ regular-izer ]] , we directly use it as our << search space >> , thus resulting in a more elegant formulation with fewer unknowns and fewer equations .


First , instead of using the model space as a regular-izer , we directly use [[ it ]] as our << search space >> , thus resulting in a more elegant formulation with fewer unknowns and fewer equations .


Third , regarding << face modeling >> , we use a very small set of [[ face metrics ]] -LRB- meaningful deformations -RRB- to parame-terize the face geometry , resulting in a smaller search space and a better posed system .


Third , regarding face modeling , we use a very small set of [[ face metrics ]] -LRB- meaningful deformations -RRB- to parame-terize the << face geometry >> , resulting in a smaller search space and a better posed system .


Third , regarding face modeling , we use a very small set of [[ face metrics ]] -LRB- meaningful deformations -RRB- to parame-terize the face geometry , resulting in a smaller << search space >> and a better posed system .


Third , regarding face modeling , we use a very small set of [[ face metrics ]] -LRB- meaningful deformations -RRB- to parame-terize the face geometry , resulting in a smaller search space and a better << posed system >> .


Experiments with both [[ synthetic and real data ]] show that this new << algorithm >> is faster , more accurate and more stable than existing ones .


Experiments with both [[ synthetic and real data ]] show that this new algorithm is faster , more accurate and more stable than existing << ones >> .


Experiments with both synthetic and real data show that this new [[ algorithm ]] is faster , more accurate and more stable than existing << ones >> .


This paper presents an [[ approach ]] to the << unsupervised learning of parts of speech >> which uses both morphological and syntactic information .


This paper presents an << approach >> to the unsupervised learning of parts of speech which uses both [[ morphological and syntactic information ]] .


While the [[ model ]] is more complex than << those >> which have been employed for unsupervised learning of POS tags in English , which use only syntactic information , the variety of languages in the world requires that we consider morphology as well .


While the model is more complex than [[ those ]] which have been employed for << unsupervised learning of POS tags in English >> , which use only syntactic information , the variety of languages in the world requires that we consider morphology as well .


While the model is more complex than << those >> which have been employed for unsupervised learning of POS tags in English , which use only [[ syntactic information ]] , the variety of languages in the world requires that we consider morphology as well .


In many languages , [[ morphology ]] provides better clues to a word 's category than << word order >> .


We present the [[ computational model ]] for << POS learning >> , and present results for applying it to Bulgarian , a Slavic language with relatively free word order and rich morphology .


We present the computational model for POS learning , and present results for applying << it >> to [[ Bulgarian ]] , a Slavic language with relatively free word order and rich morphology .


We present the computational model for POS learning , and present results for applying it to [[ Bulgarian ]] , a << Slavic language >> with relatively free word order and rich morphology .


We present the computational model for POS learning , and present results for applying it to << Bulgarian >> , a Slavic language with relatively [[ free word order ]] and rich morphology .


We present the computational model for POS learning , and present results for applying it to Bulgarian , a Slavic language with relatively [[ free word order ]] and << rich morphology >> .


We present the computational model for POS learning , and present results for applying it to << Bulgarian >> , a Slavic language with relatively free word order and [[ rich morphology ]] .


In << MT >> , the widely used approach is to apply a [[ Chinese word segmenter ]] trained from manually annotated data , using a fixed lexicon .


In MT , the widely used approach is to apply a << Chinese word segmenter >> trained from [[ manually annotated data ]] , using a fixed lexicon .


Such [[ word segmentation ]] is not necessarily optimal for << translation >> .


We propose a [[ Bayesian semi-supervised Chinese word segmentation model ]] which uses both monolingual and bilingual information to derive a << segmentation >> suitable for MT .


We propose a << Bayesian semi-supervised Chinese word segmentation model >> which uses both [[ monolingual and bilingual information ]] to derive a segmentation suitable for MT .


We propose a Bayesian semi-supervised Chinese word segmentation model which uses both monolingual and bilingual information to derive a [[ segmentation ]] suitable for << MT >> .


Experiments show that our [[ method ]] improves a state-of-the-art << MT system >> in a small and a large data environment .


In this paper we compare two competing [[ approaches ]] to << part-of-speech tagging >> , statistical and constraint-based disambiguation , using French as our test language .


In this paper we compare two competing << approaches >> to part-of-speech tagging , statistical and constraint-based disambiguation , using [[ French ]] as our test language .


We imposed a time limit on our experiment : the amount of time spent on the design of our [[ constraint system ]] was about the same as the time we used to train and test the easy-to-implement << statistical model >> .


The [[ accuracy ]] of the << statistical method >> is reasonably good , comparable to taggers for English .


The [[ accuracy ]] of the statistical method is reasonably good , comparable to << taggers >> for English .


The accuracy of the [[ statistical method ]] is reasonably good , comparable to << taggers >> for English .


The accuracy of the statistical method is reasonably good , comparable to [[ taggers ]] for << English >> .


[[ Structured-light methods ]] actively generate << geometric correspondence data >> between projectors and cameras in order to facilitate robust 3D reconstruction .


Structured-light methods actively generate [[ geometric correspondence data ]] between projectors and cameras in order to facilitate << robust 3D reconstruction >> .


In this paper , we present << Photogeometric Structured Light >> whereby a standard [[ structured light method ]] is extended to include photometric methods .


In this paper , we present << Photogeometric Structured Light >> whereby a standard structured light method is extended to include [[ photometric methods ]] .


[[ Photometric processing ]] serves the double purpose of increasing the amount of << recovered surface detail >> and of enabling the structured-light setup to be robustly self-calibrated .


[[ Photometric processing ]] serves the double purpose of increasing the amount of recovered surface detail and of enabling the << structured-light setup >> to be robustly self-calibrated .


Further , our << framework >> uses a [[ photogeometric optimization ]] that supports the simultaneous use of multiple cameras and projectors and yields a single and accurate multi-view 3D model which best complies with photometric and geometric data .


Further , our framework uses a photogeometric optimization that supports the simultaneous use of multiple cameras and projectors and yields a single and accurate << multi-view 3D model >> which best complies with [[ photometric and geometric data ]] .


In this paper , a discrimination and robustness oriented [[ adaptive learning procedure ]] is proposed to deal with the task of << syntactic ambiguity resolution >> .


Owing to the problem of [[ insufficient training data ]] and << approximation error >> introduced by the language model , traditional statistical approaches , which resolve ambiguities by indirectly and implicitly using maximum likelihood method , fail to achieve high performance in real applications .


Owing to the problem of insufficient training data and approximation error introduced by the language model , traditional [[ statistical approaches ]] , which resolve << ambiguities >> by indirectly and implicitly using maximum likelihood method , fail to achieve high performance in real applications .


Owing to the problem of insufficient training data and approximation error introduced by the language model , traditional << statistical approaches >> , which resolve ambiguities by indirectly and implicitly using [[ maximum likelihood method ]] , fail to achieve high performance in real applications .


The [[ accuracy rate ]] of << syntactic disambiguation >> is raised from 46.0 % to 60.62 % by using this novel approach .


The accuracy rate of [[ syntactic disambiguation ]] is raised from 46.0 % to 60.62 % by using this novel << approach >> .


This paper presents a new [[ approach ]] to << statistical sentence generation >> in which alternative phrases are represented as packed sets of trees , or forests , and then ranked statistically to choose the best one .


[[ It ]] also facilitates more efficient << statistical ranking >> than a previous approach to statistical generation .


[[ It ]] also facilitates more efficient statistical ranking than a previous << approach >> to statistical generation .


It also facilitates more efficient statistical ranking than a previous [[ approach ]] to << statistical generation >> .


An efficient [[ ranking algorithm ]] is described , together with experimental results showing significant improvements over simple << enumeration >> or a lattice-based approach .


An efficient [[ ranking algorithm ]] is described , together with experimental results showing significant improvements over simple enumeration or a << lattice-based approach >> .


An efficient ranking algorithm is described , together with experimental results showing significant improvements over simple [[ enumeration ]] or a << lattice-based approach >> .


This article deals with the interpretation of conceptual operations underlying the communicative use of [[ natural language -LRB- NL -RRB- ]] within the << Structured Inheritance Network -LRB- SI-Nets -RRB- paradigm >> .


The operations are reduced to functions of a formal language , thus changing the level of abstraction of the [[ operations ]] to be performed on << SI-Nets >> .


In this sense , [[ operations ]] on << SI-Nets >> are not merely isomorphic to single epistemological objects , but can be viewed as a simulation of processes on a different level , that pertaining to the conceptual system of NL .


In this sense , operations on SI-Nets are not merely isomorphic to single epistemological objects , but can be viewed as a simulation of processes on a different level , that pertaining to the << conceptual system >> of [[ NL ]] .


For this purpose , we have designed a version of [[ KL-ONE ]] which represents the epistemological level , while the new experimental language , << KL-Conc >> , represents the conceptual level .


For this purpose , we have designed a version of << KL-ONE >> which represents the [[ epistemological level ]] , while the new experimental language , KL-Conc , represents the conceptual level .


For this purpose , we have designed a version of KL-ONE which represents the epistemological level , while the new experimental language , << KL-Conc >> , represents the [[ conceptual level ]] .


We present an [[ algorithm ]] for << calibrated camera relative pose estimation >> from lines .


We evaluate the performance of the << algorithm >> using [[ synthetic and real data ]] .


The intended use of the [[ algorithm ]] is with robust << hypothesize-and-test frameworks >> such as RANSAC .


The intended use of the algorithm is with robust << hypothesize-and-test frameworks >> such as [[ RANSAC ]] .


Our [[ approach ]] is suitable for << urban and indoor environments >> where most lines are either parallel or orthogonal to each other .


In this paper , we present a [[ fully automated extraction system ]] , named IntEx , to identify << gene and protein interactions >> in biomedical text .


In this paper , we present a << fully automated extraction system >> , named [[ IntEx ]] , to identify gene and protein interactions in biomedical text .


In this paper , we present a fully automated extraction system , named IntEx , to identify << gene and protein interactions >> in [[ biomedical text ]] .


Then , tagging << biological entities >> with the help of [[ biomedical and linguistic ontologies ]] .


Our [[ extraction system ]] handles complex sentences and extracts << multiple and nested interactions >> specified in a sentence .


Experimental evaluations with two other state of the art << extraction systems >> indicate that the [[ IntEx system ]] achieves better performance without the labor intensive pattern engineering requirement .


This paper introduces a [[ method ]] for << computational analysis of move structures >> in abstracts of research articles .


This paper introduces a method for << computational analysis of move structures >> in [[ abstracts of research articles ]] .


The method involves automatically gathering a large number of << abstracts >> from the [[ Web ]] and building a language model of abstract moves .


The method involves automatically gathering a large number of abstracts from the Web and building a << language model >> of [[ abstract moves ]] .


We also present a << prototype concordancer >> , [[ CARE ]] , which exploits the move-tagged abstracts for digital learning .


We also present a prototype concordancer , [[ CARE ]] , which exploits the << move-tagged abstracts >> for digital learning .


We also present a prototype concordancer , CARE , which exploits the [[ move-tagged abstracts ]] for << digital learning >> .


This [[ system ]] provides a promising << approach >> to Web-based computer-assisted academic writing .


This system provides a promising [[ approach ]] to << Web-based computer-assisted academic writing >> .


This work presents a [[ real-time system ]] for << multiple object tracking in dynamic scenes >> .


A unique characteristic of the [[ system ]] is its ability to cope with << long-duration and complete occlusion >> without a prior knowledge about the shape or motion of objects .


A unique characteristic of the system is its ability to cope with long-duration and complete occlusion without a [[ prior knowledge ]] about the << shape >> or motion of objects .


A unique characteristic of the system is its ability to cope with long-duration and complete occlusion without a [[ prior knowledge ]] about the shape or << motion of objects >> .


A unique characteristic of the system is its ability to cope with long-duration and complete occlusion without a prior knowledge about the [[ shape ]] or << motion of objects >> .


The << system >> produces good segment and [[ tracking ]] results at a frame rate of 15-20 fps for image size of 320x240 , as demonstrated by extensive experiments performed using video sequences under different conditions indoor and outdoor with long-duration and complete occlusions in changing background .


We propose a [[ method ]] of << organizing reading materials >> for vocabulary learning .


We propose a method of [[ organizing reading materials ]] for << vocabulary learning >> .


We used a specialized vocabulary for an English certification test as the target vocabulary and used [[ English Wikipedia ]] , a << free-content encyclopedia >> , as the target corpus .


A novel [[ bootstrapping approach ]] to << Named Entity -LRB- NE -RRB- tagging >> using concept-based seeds and successive learners is presented .


A novel << bootstrapping approach >> to Named Entity -LRB- NE -RRB- tagging using [[ concept-based seeds ]] and successive learners is presented .


A novel bootstrapping approach to Named Entity -LRB- NE -RRB- tagging using [[ concept-based seeds ]] and << successive learners >> is presented .


A novel << bootstrapping approach >> to Named Entity -LRB- NE -RRB- tagging using concept-based seeds and [[ successive learners ]] is presented .


This approach only requires a few common noun or pronoun seeds that correspond to the concept for the targeted << NE >> , e.g. he/she/man / woman for [[ PERSON NE ]] .


The << bootstrapping procedure >> is implemented as training two [[ successive learners ]] .


First , [[ decision list ]] is used to learn the << parsing-based NE rules >> .


The resulting [[ NE system ]] approaches << supervised NE >> performance for some NE types .


We present the first known empirical test of an increasingly common speculative claim , by evaluating a representative << Chinese-to-English SMT model >> directly on [[ word sense disambiguation ]] performance , using standard WSD evaluation methodology and datasets from the Senseval-3 Chinese lexical sample task .


We present the first known empirical test of an increasingly common speculative claim , by evaluating a representative << Chinese-to-English SMT model >> directly on word sense disambiguation performance , using standard [[ WSD evaluation methodology ]] and datasets from the Senseval-3 Chinese lexical sample task .


We present the first known empirical test of an increasingly common speculative claim , by evaluating a representative << Chinese-to-English SMT model >> directly on word sense disambiguation performance , using standard WSD evaluation methodology and datasets from the [[ Senseval-3 Chinese lexical sample task ]] .


Much effort has been put in designing and evaluating << dedicated word sense disambiguation -LRB- WSD -RRB- models >> , in particular with the [[ Senseval series of workshops ]] .


At the same time , the recent improvements in the [[ BLEU scores ]] of << statistical machine translation -LRB- SMT -RRB- >> suggests that SMT models are good at predicting the right translation of the words in source language sentences .


At the same time , the recent improvements in the BLEU scores of statistical machine translation -LRB- SMT -RRB- suggests that [[ SMT models ]] are good at predicting the right << translation >> of the words in source language sentences .


Surprisingly however , the [[ WSD accuracy ]] of << SMT models >> has never been evaluated and compared with that of the dedicated WSD models .


Surprisingly however , the << WSD accuracy >> of SMT models has never been evaluated and compared with [[ that ]] of the dedicated WSD models .


We present controlled experiments showing the [[ WSD accuracy ]] of current typical << SMT models >> to be significantly lower than that of all the dedicated WSD models considered .


We present controlled experiments showing the << WSD accuracy >> of current typical SMT models to be significantly lower than [[ that ]] of all the dedicated WSD models considered .


This tends to support the view that despite recent speculative claims to the contrary , current [[ SMT models ]] do have limitations in comparison with << dedicated WSD models >> , and that SMT should benefit from the better predictions made by the WSD models .


This tends to support the view that despite recent speculative claims to the contrary , current SMT models do have limitations in comparison with dedicated WSD models , and that << SMT >> should benefit from the better predictions made by the [[ WSD models ]] .


In this paper we present a novel , customizable : << IE paradigm >> that takes advantage of [[ predicate-argument structures ]] .


<< It >> is based on : -LRB- 1 -RRB- an extended set of [[ features ]] ; and -LRB- 2 -RRB- inductive decision tree learning .


It is based on : -LRB- 1 -RRB- an extended set of [[ features ]] ; and -LRB- 2 -RRB- << inductive decision tree learning >> .


<< It >> is based on : -LRB- 1 -RRB- an extended set of features ; and -LRB- 2 -RRB- [[ inductive decision tree learning ]] .


The experimental results prove our claim that accurate [[ predicate-argument structures ]] enable high quality << IE >> results .


In this paper we present a [[ statistical profile ]] of the << Named Entity task >> , a specific information extraction task for which corpora in several languages are available .


In this paper we present a statistical profile of the [[ Named Entity task ]] , a specific << information extraction task >> for which corpora in several languages are available .


Using the results of the [[ statistical analysis ]] , we propose an << algorithm >> for lower bound estimation for Named Entity corpora and discuss the significance of the cross-lingual comparisons provided by the analysis .


Using the results of the statistical analysis , we propose an [[ algorithm ]] for << lower bound estimation >> for Named Entity corpora and discuss the significance of the cross-lingual comparisons provided by the analysis .


Using the results of the statistical analysis , we propose an algorithm for [[ lower bound estimation ]] for << Named Entity corpora >> and discuss the significance of the cross-lingual comparisons provided by the analysis .


We attack an inexplicably << under-explored language genre of spoken language >> -- [[ lyrics in music ]] -- via completely unsuper-vised induction of an SMT-style stochastic transduction grammar for hip hop lyrics , yielding a fully-automatically learned challenge-response system that produces rhyming lyrics given an input .


We attack an inexplicably << under-explored language genre of spoken language >> -- lyrics in music -- via completely [[ unsuper-vised induction ]] of an SMT-style stochastic transduction grammar for hip hop lyrics , yielding a fully-automatically learned challenge-response system that produces rhyming lyrics given an input .


We attack an inexplicably under-explored language genre of spoken language -- lyrics in music -- via completely [[ unsuper-vised induction ]] of an << SMT-style stochastic transduction grammar >> for hip hop lyrics , yielding a fully-automatically learned challenge-response system that produces rhyming lyrics given an input .


We attack an inexplicably under-explored language genre of spoken language -- lyrics in music -- via completely [[ unsuper-vised induction ]] of an SMT-style stochastic transduction grammar for hip hop lyrics , yielding a << fully-automatically learned challenge-response system >> that produces rhyming lyrics given an input .


We attack an inexplicably under-explored language genre of spoken language -- lyrics in music -- via completely unsuper-vised induction of an << SMT-style stochastic transduction grammar >> for [[ hip hop lyrics ]] , yielding a fully-automatically learned challenge-response system that produces rhyming lyrics given an input .


We attack an inexplicably under-explored language genre of spoken language -- lyrics in music -- via completely unsuper-vised induction of an SMT-style stochastic transduction grammar for hip hop lyrics , yielding a [[ fully-automatically learned challenge-response system ]] that produces << rhyming lyrics >> given an input .


In spite of the level of difficulty of the challenge , the [[ model ]] nevertheless produces fluent output as judged by human evaluators , and performs significantly better than widely used << phrase-based SMT models >> upon the same task .


In spite of the level of difficulty of the challenge , the << model >> nevertheless produces fluent output as judged by human evaluators , and performs significantly better than widely used phrase-based SMT models upon the same [[ task ]] .


In spite of the level of difficulty of the challenge , the model nevertheless produces fluent output as judged by human evaluators , and performs significantly better than widely used << phrase-based SMT models >> upon the same [[ task ]] .


In this paper , we investigate the problem of automatically << predicting segment boundaries >> in [[ spoken multiparty dialogue ]] .


We first apply [[ approaches ]] that have been proposed for << predicting top-level topic shifts >> to the problem of identifying subtopic boundaries .


We first apply [[ approaches ]] that have been proposed for predicting top-level topic shifts to the problem of << identifying subtopic boundaries >> .


We first apply approaches that have been proposed for [[ predicting top-level topic shifts ]] to the problem of << identifying subtopic boundaries >> .


We then explore the impact on performance of using [[ ASR output ]] as opposed to << human transcription >> .


Examination of the effect of features shows that << predicting top-level and predicting subtopic boundaries >> are two distinct tasks : -LRB- 1 -RRB- for [[ predicting subtopic boundaries ]] , the lexical cohesion-based approach alone can achieve competitive results , -LRB- 2 -RRB- for predicting top-level boundaries , the machine learning approach that combines lexical-cohesion and conversational features performs best , and -LRB- 3 -RRB- conversational cues , such as cue phrases and overlapping speech , are better indicators for the top-level prediction task .


Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks : -LRB- 1 -RRB- for << predicting subtopic boundaries >> , the [[ lexical cohesion-based approach ]] alone can achieve competitive results , -LRB- 2 -RRB- for predicting top-level boundaries , the machine learning approach that combines lexical-cohesion and conversational features performs best , and -LRB- 3 -RRB- conversational cues , such as cue phrases and overlapping speech , are better indicators for the top-level prediction task .


Examination of the effect of features shows that << predicting top-level and predicting subtopic boundaries >> are two distinct tasks : -LRB- 1 -RRB- for predicting subtopic boundaries , the lexical cohesion-based approach alone can achieve competitive results , -LRB- 2 -RRB- for [[ predicting top-level boundaries ]] , the machine learning approach that combines lexical-cohesion and conversational features performs best , and -LRB- 3 -RRB- conversational cues , such as cue phrases and overlapping speech , are better indicators for the top-level prediction task .