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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 .