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The extension is shown to be sufficient to provide a strongly adequate [[ grammar ]] for << crossed serial dependencies >> , as found in e.g. Dutch subordinate clauses .

The << extension >> is shown to be parseable by a simple [[ extension ]] to an existing parsing method for GPSG .

The extension is shown to be parseable by a simple << extension >> to an existing [[ parsing method ]] for GPSG .

The extension is shown to be parseable by a simple extension to an existing [[ parsing method ]] for << GPSG >> .

This paper presents an [[ approach ]] to << localizing functional objects >> in surveillance videos without domain knowledge about semantic object classes that may appear in the scene .

This paper presents an approach to << localizing functional objects >> in [[ surveillance videos ]] without domain knowledge about semantic object classes that may appear in the scene .

This paper presents an approach to localizing functional objects in surveillance videos without << domain knowledge >> about [[ semantic object classes ]] that may appear in the scene .

A [[ Bayesian framework ]] is used to probabilistically model : << people 's trajectories and intents >> , constraint map of the scene , and locations of functional objects .

A [[ Bayesian framework ]] is used to probabilistically model : people 's trajectories and intents , << constraint map of the scene >> , and locations of functional objects .

A [[ Bayesian framework ]] is used to probabilistically model : people 's trajectories and intents , constraint map of the scene , and << locations of functional objects >> .

A Bayesian framework is used to probabilistically model : [[ people 's trajectories and intents ]] , << constraint map of the scene >> , and locations of functional objects .

A Bayesian framework is used to probabilistically model : people 's trajectories and intents , [[ constraint map of the scene ]] , and << locations of functional objects >> .

A [[ data-driven Markov Chain Monte Carlo -LRB- MCMC -RRB- process ]] is used for << inference >> .

Our evaluation on [[ videos of public squares and courtyards ]] demonstrates our effectiveness in << localizing functional objects >> and predicting people 's trajectories in unobserved parts of the video footage .

Our evaluation on [[ videos of public squares and courtyards ]] demonstrates our effectiveness in localizing functional objects and << predicting people 's trajectories >> in unobserved parts of the video footage .

Our evaluation on videos of public squares and courtyards demonstrates our effectiveness in [[ localizing functional objects ]] and << predicting people 's trajectories >> in unobserved parts of the video footage .

We propose a [[ process model ]] for << hierarchical perceptual sound organization >> , which recognizes perceptual sounds included in incoming sound signals .

We propose a process model for hierarchical perceptual sound organization , which recognizes [[ perceptual sounds ]] included in << incoming sound signals >> .

We consider << perceptual sound organization >> as a [[ scene analysis problem ]] in the auditory domain .

We consider perceptual sound organization as a << scene analysis problem >> in the [[ auditory domain ]] .

Our << model >> consists of multiple [[ processing modules ]] and a hypothesis network for quantitative integration of multiple sources of information .

Our model consists of multiple [[ processing modules ]] and a << hypothesis network >> for quantitative integration of multiple sources of information .

Our << model >> consists of multiple processing modules and a [[ hypothesis network ]] for quantitative integration of multiple sources of information .

On the << hypothesis network >> , individual information is integrated and an optimal [[ internal model ]] of perceptual sounds is automatically constructed .

On the hypothesis network , individual information is integrated and an optimal [[ internal model ]] of << perceptual sounds >> is automatically constructed .

Based on the model , a [[ music scene analysis system ]] has been developed for << acoustic signals of ensemble music >> , which recognizes rhythm , chords , and source-separated musical notes .

Based on the model , a [[ music scene analysis system ]] has been developed for acoustic signals of ensemble music , which recognizes << rhythm >> , chords , and source-separated musical notes .

Based on the model , a [[ music scene analysis system ]] has been developed for acoustic signals of ensemble music , which recognizes rhythm , << chords >> , and source-separated musical notes .

Based on the model , a [[ music scene analysis system ]] has been developed for acoustic signals of ensemble music , which recognizes rhythm , chords , and << source-separated musical notes >> .

Based on the model , a music scene analysis system has been developed for acoustic signals of ensemble music , which recognizes [[ rhythm ]] , << chords >> , and source-separated musical notes .

Based on the model , a music scene analysis system has been developed for acoustic signals of ensemble music , which recognizes rhythm , [[ chords ]] , and << source-separated musical notes >> .

Experimental results show that our << method >> has permitted autonomous , stable and effective [[ information integration ]] to construct the internal model of hierarchical perceptual sounds .

Experimental results show that our method has permitted autonomous , stable and effective [[ information integration ]] to construct the << internal model >> of hierarchical perceptual sounds .

Experimental results show that our method has permitted autonomous , stable and effective information integration to construct the [[ internal model ]] of << hierarchical perceptual sounds >> .

We directly investigate a subject of much recent debate : do [[ word sense disambigation models ]] help << statistical machine translation quality >> ?

Using a state-of-the-art [[ Chinese word sense disambiguation model ]] to choose << translation candidates >> for a typical IBM statistical MT system , we find that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone .

Using a state-of-the-art Chinese word sense disambiguation model to choose [[ translation candidates ]] for a typical << IBM statistical MT system >> , we find that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone .

Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system , we find that [[ word sense disambiguation ]] does not yield significantly better translation quality than the << statistical machine translation system >> alone .

Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system , we find that << word sense disambiguation >> does not yield significantly better [[ translation quality ]] than the statistical machine translation system alone .

Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system , we find that word sense disambiguation does not yield significantly better [[ translation quality ]] than the << statistical machine translation system >> alone .

[[ Image sequence processing techniques ]] are used to study << exchange , growth , and transport processes >> and to tackle key questions in environmental physics and biology .

Image sequence processing techniques are used to study exchange , growth , and transport processes and to tackle key questions in [[ environmental physics ]] and << biology >> .

These applications require high [[ accuracy ]] for the << estimation of the motion field >> since the most interesting parameters of the dynamical processes studied are contained in first-order derivatives of the motion field or in dynamical changes of the moving objects .

These << applications >> require high accuracy for the [[ estimation of the motion field ]] since the most interesting parameters of the dynamical processes studied are contained in first-order derivatives of the motion field or in dynamical changes of the moving objects .

These applications require high accuracy for the estimation of the motion field since the most interesting parameters of the dynamical processes studied are contained in [[ first-order derivatives of the motion field ]] or in << dynamical changes of the moving objects >> .

A << tensor method >> tuned with carefully optimized [[ derivative filters ]] yields reliable and dense displacement vector fields -LRB- DVF -RRB- with an accuracy of up to a few hundredth pixels/frame for real-world images .

A tensor method tuned with carefully optimized derivative filters yields reliable and dense << displacement vector fields -LRB- DVF -RRB- >> with an accuracy of up to a few hundredth [[ pixels/frame ]] for real-world images .

A tensor method tuned with carefully optimized derivative filters yields reliable and dense displacement vector fields -LRB- DVF -RRB- with an accuracy of up to a few hundredth << pixels/frame >> for [[ real-world images ]] .

The [[ accuracy ]] of the << tensor method >> is verified with computer-generated sequences and a calibrated image sequence .

The accuracy of the << tensor method >> is verified with [[ computer-generated sequences ]] and a calibrated image sequence .

The accuracy of the tensor method is verified with [[ computer-generated sequences ]] and a << calibrated image sequence >> .

The accuracy of the << tensor method >> is verified with computer-generated sequences and a [[ calibrated image sequence ]] .

With the improvements in [[ accuracy ]] the << motion estimation >> is now rather limited by imperfections in the CCD sensors , especially the spatial nonuni-formity in the responsivity .

With the improvements in accuracy the << motion estimation >> is now rather limited by imperfections in the [[ CCD sensors ]] , especially the spatial nonuni-formity in the responsivity .

With the improvements in accuracy the motion estimation is now rather limited by imperfections in the CCD sensors , especially the [[ spatial nonuni-formity ]] in the << responsivity >> .

With the improvements in accuracy the motion estimation is now rather limited by imperfections in the << CCD sensors >> , especially the spatial nonuni-formity in the [[ responsivity ]] .

The application of the [[ techniques ]] to the << analysis of plant growth >> , to ocean surface microturbulence in IR image sequences , and to sediment transport is demonstrated .

The application of the [[ techniques ]] to the analysis of plant growth , to << ocean surface microturbulence in IR image sequences >> , and to sediment transport is demonstrated .

The application of the [[ techniques ]] to the analysis of plant growth , to ocean surface microturbulence in IR image sequences , and to << sediment transport >> is demonstrated .

The application of the techniques to the [[ analysis of plant growth ]] , to << ocean surface microturbulence in IR image sequences >> , and to sediment transport is demonstrated .

The application of the techniques to the analysis of plant growth , to [[ ocean surface microturbulence in IR image sequences ]] , and to << sediment transport >> is demonstrated .

We present a [[ Czech-English statistical machine translation system ]] which performs << tree-to-tree translation of dependency structures >> .

The only << bilingual resource >> required is a [[ sentence-aligned parallel corpus ]] .

We also refer to an evaluation method and plan to compare our [[ system ]] 's output with a << benchmark system >> .

This paper describes the understanding process of the << spatial descriptions >> in [[ Japanese ]] .

To reconstruct the model , the authors extract the qualitative spatial constraints from the text , and represent them as the << numerical constraints >> on the [[ spatial attributes of the entities ]] .

Such [[ context information ]] is therefore important to characterize the << intrinsic representation of a video frame >> .

In this paper , we present a novel [[ approach ]] to learn the << deep video representation >> by exploring both local and holistic contexts .

In this paper , we present a novel << approach >> to learn the deep video representation by exploring both [[ local and holistic contexts ]] .

Specifically , we propose a [[ triplet sampling mechanism ]] to encode the << local temporal relationship of adjacent frames >> based on their deep representations .

Specifically , we propose a << triplet sampling mechanism >> to encode the local temporal relationship of adjacent frames based on their [[ deep representations ]] .

In addition , we incorporate the [[ graph structure of the video ]] , as a << priori >> , to holistically preserve the inherent correlations among video frames .

Our << approach >> is fully unsupervised and trained in an [[ end-to-end deep convolutional neu-ral network architecture ]] .

By extensive experiments , we show that our [[ learned representation ]] can significantly boost several video recognition tasks -LRB- retrieval , classification , and highlight detection -RRB- over traditional << video representations >> .

By extensive experiments , we show that our << learned representation >> can significantly boost several [[ video recognition tasks ]] -LRB- retrieval , classification , and highlight detection -RRB- over traditional video representations .

By extensive experiments , we show that our learned representation can significantly boost several [[ video recognition tasks ]] -LRB- retrieval , classification , and highlight detection -RRB- over traditional << video representations >> .

By extensive experiments , we show that our learned representation can significantly boost several << video recognition tasks >> -LRB- [[ retrieval ]] , classification , and highlight detection -RRB- over traditional video representations .

By extensive experiments , we show that our learned representation can significantly boost several video recognition tasks -LRB- [[ retrieval ]] , << classification >> , and highlight detection -RRB- over traditional video representations .

By extensive experiments , we show that our learned representation can significantly boost several << video recognition tasks >> -LRB- retrieval , [[ classification ]] , and highlight detection -RRB- over traditional video representations .

By extensive experiments , we show that our learned representation can significantly boost several video recognition tasks -LRB- retrieval , [[ classification ]] , and << highlight detection >> -RRB- over traditional video representations .

By extensive experiments , we show that our learned representation can significantly boost several << video recognition tasks >> -LRB- retrieval , classification , and [[ highlight detection ]] -RRB- over traditional video representations .

For << mobile speech application >> , [[ speaker DOA estimation accuracy ]] , interference robustness and compact physical size are three key factors .

For mobile speech application , [[ speaker DOA estimation accuracy ]] , << interference robustness >> and compact physical size are three key factors .

For << mobile speech application >> , speaker DOA estimation accuracy , [[ interference robustness ]] and compact physical size are three key factors .

For mobile speech application , speaker DOA estimation accuracy , [[ interference robustness ]] and << compact physical size >> are three key factors .

For << mobile speech application >> , speaker DOA estimation accuracy , interference robustness and [[ compact physical size ]] are three key factors .

[[ It ]] is achieved by deriving the inter-sensor data ratio model of an AVS in bispectrum domain -LRB- BISDR -RRB- and exploring the << favorable properties >> of bispectrum , such as zero value of Gaussian process and different distribution of speech and NSI .

It is achieved by deriving the [[ inter-sensor data ratio model ]] of an << AVS >> in bispectrum domain -LRB- BISDR -RRB- and exploring the favorable properties of bispectrum , such as zero value of Gaussian process and different distribution of speech and NSI .

It is achieved by deriving the inter-sensor data ratio model of an << AVS >> in [[ bispectrum domain -LRB- BISDR -RRB- ]] and exploring the favorable properties of bispectrum , such as zero value of Gaussian process and different distribution of speech and NSI .

It is achieved by deriving the inter-sensor data ratio model of an AVS in bispectrum domain -LRB- BISDR -RRB- and exploring the << favorable properties >> of bispectrum , such as [[ zero value of Gaussian process ]] and different distribution of speech and NSI .

It is achieved by deriving the inter-sensor data ratio model of an AVS in bispectrum domain -LRB- BISDR -RRB- and exploring the favorable properties of bispectrum , such as [[ zero value of Gaussian process ]] and different << distribution of speech and NSI >> .

It is achieved by deriving the inter-sensor data ratio model of an AVS in bispectrum domain -LRB- BISDR -RRB- and exploring the << favorable properties >> of bispectrum , such as zero value of Gaussian process and different [[ distribution of speech and NSI ]] .

Specifically , a reliable [[ bispectrum mask ]] is generated to guarantee that the << speaker DOA cues >> , derived from BISDR , are robust to NSI in terms of speech sparsity and large bispectrum amplitude of the captured signals .

Specifically , a reliable bispectrum mask is generated to guarantee that the << speaker DOA cues >> , derived from [[ BISDR ]] , are robust to NSI in terms of speech sparsity and large bispectrum amplitude of the captured signals .

Intensive experiments demonstrate an improved performance of our proposed [[ algorithm ]] under various << NSI conditions >> even when SIR is smaller than 0dB .

In this paper , we want to show how the [[ morphological component ]] of an existing << NLP-system for Dutch -LRB- Dutch Medical Language Processor - DMLP -RRB- >> has been extended in order to produce output that is compatible with the language independent modules of the LSP-MLP system -LRB- Linguistic String Project - Medical Language Processor -RRB- of the New York University .

In this paper , we want to show how the morphological component of an existing NLP-system for Dutch -LRB- Dutch Medical Language Processor - DMLP -RRB- has been extended in order to produce output that is compatible with the [[ language independent modules ]] of the << LSP-MLP system -LRB- Linguistic String Project - Medical Language Processor -RRB- >> of the New York University .

The << former >> can take advantage of the language independent developments of the [[ latter ]] , while focusing on idiosyncrasies for Dutch .

The former can take advantage of the language independent developments of the latter , while focusing on << idiosyncrasies >> for [[ Dutch ]] .

This general strategy will be illustrated by a practical application , namely the highlighting of [[ relevant information ]] in a << patient discharge summary -LRB- PDS -RRB- >> by means of modern HyperText Mark-Up Language -LRB- HTML -RRB- technology .