shweta2911/SCIERC · Datasets at Hugging Face

text stringlengths 49 577	label stringclasses 7 values	metadata sequence
[[ Honorifics ]] are used extensively in << Japanese >> , reflecting the social relationship -LRB- e.g. social ranks and age -RRB- of the referents .	USED-FOR	[ 0, 0, 5, 5 ]
This [[ referential information ]] is vital for resolving << zero pronouns >> and improving machine translation outputs .	USED-FOR	[ 1, 2, 7, 8 ]
This [[ referential information ]] is vital for resolving zero pronouns and improving << machine translation outputs >> .	USED-FOR	[ 1, 2, 11, 13 ]
<< Visually-guided arm reaching movements >> are produced by [[ distributed neural networks ]] within parietal and frontal regions of the cerebral cortex .	USED-FOR	[ 7, 9, 0, 3 ]
Experimental data indicate that -LRB- I -RRB- single neurons in these regions are broadly tuned to parameters of movement ; -LRB- 2 -RRB- appropriate commands are elaborated by populations of neurons ; -LRB- 3 -RRB- the << coordinated action of neu-rons >> can be visualized using a [[ neuronal population vector -LRB- NPV -RRB- ]] .	USED-FOR	[ 45, 50, 36, 39 ]
We designed a [[ model ]] of the << cortical motor command >> to investigate the relation between the desired direction of the movement , the actual direction of movement and the direction of the NPV in motor cortex .	USED-FOR	[ 3, 3, 6, 8 ]
We designed a model of the cortical motor command to investigate the relation between the desired direction of the movement , the actual direction of movement and the direction of the [[ NPV ]] in << motor cortex >> .	USED-FOR	[ 31, 31, 33, 34 ]
The model is a [[ two-layer self-organizing neural network ]] which combines broadly-tuned -LRB- muscular -RRB- proprioceptive and -LRB- cartesian -RRB- visual information to calculate << -LRB- angular -RRB- motor commands >> for the initial part of the movement of a two-link arm .	USED-FOR	[ 4, 7, 23, 27 ]
The model is a << two-layer self-organizing neural network >> which combines [[ broadly-tuned -LRB- muscular -RRB- proprioceptive ]] and -LRB- cartesian -RRB- visual information to calculate -LRB- angular -RRB- motor commands for the initial part of the movement of a two-link arm .	USED-FOR	[ 10, 14, 4, 7 ]
The model is a two-layer self-organizing neural network which combines [[ broadly-tuned -LRB- muscular -RRB- proprioceptive ]] and << -LRB- cartesian -RRB- visual information >> to calculate -LRB- angular -RRB- motor commands for the initial part of the movement of a two-link arm .	CONJUNCTION	[ 10, 14, 16, 20 ]
The model is a << two-layer self-organizing neural network >> which combines broadly-tuned -LRB- muscular -RRB- proprioceptive and [[ -LRB- cartesian -RRB- visual information ]] to calculate -LRB- angular -RRB- motor commands for the initial part of the movement of a two-link arm .	USED-FOR	[ 16, 20, 4, 7 ]
These results suggest the NPV does not give a faithful << image of cortical processing >> during [[ arm reaching movements ]] .	FEATURE-OF	[ 15, 17, 10, 13 ]
It is well-known that diversity among [[ base classifiers ]] is crucial for constructing a strong << ensemble >> .	USED-FOR	[ 6, 7, 14, 14 ]
In this paper , we propose an alternative way for << ensemble construction >> by [[ resampling pairwise constraints ]] that specify whether a pair of instances belongs to the same class or not .	USED-FOR	[ 13, 15, 10, 11 ]
Using [[ pairwise constraints ]] for << ensemble construction >> is challenging because it remains unknown how to influence the base classifiers with the sampled pairwise constraints .	USED-FOR	[ 1, 2, 4, 5 ]
First , we transform the original instances into a new << data representation >> using [[ projections ]] learnt from pairwise constraints .	USED-FOR	[ 13, 13, 10, 11 ]
First , we transform the original instances into a new data representation using << projections >> learnt from [[ pairwise constraints ]] .	USED-FOR	[ 16, 17, 13, 13 ]
Then , we build the << base clas-sifiers >> with the new [[ data representation ]] .	USED-FOR	[ 10, 11, 5, 6 ]
We propose two methods for << resampling pairwise constraints >> following the standard [[ Bagging and Boosting algorithms ]] , respectively .	USED-FOR	[ 11, 14, 5, 7 ]
A new [[ algorithm ]] for solving the three << dimensional container packing problem >> is proposed in this paper .	USED-FOR	[ 2, 2, 7, 10 ]
This new [[ algorithm ]] deviates from the traditional << approach of wall building and layering >> .	COMPARE	[ 2, 2, 7, 12 ]
We tested our << method >> using all 760 test cases from the [[ OR-Library ]] .	EVALUATE-FOR	[ 11, 11, 3, 3 ]
Experimental results indicate that the new << algorithm >> is able to achieve an [[ average packing utilization ]] of more than 87 % .	EVALUATE-FOR	[ 12, 14, 6, 6 ]
Current [[ approaches ]] to << object category recognition >> require datasets of training images to be manually prepared , with varying degrees of supervision .	USED-FOR	[ 1, 1, 3, 5 ]
Current << approaches >> to object category recognition require [[ datasets ]] of training images to be manually prepared , with varying degrees of supervision .	USED-FOR	[ 7, 7, 1, 1 ]
We present an [[ approach ]] that can learn an << object category >> from just its name , by utilizing the raw output of image search engines available on the Internet .	USED-FOR	[ 3, 3, 8, 9 ]
We develop a new model , << TSI-pLSA >> , which extends [[ pLSA ]] -LRB- as applied to visual words -RRB- to include spatial information in a translation and scale invariant manner .	USED-FOR	[ 10, 10, 6, 6 ]
We develop a new model , TSI-pLSA , which extends [[ pLSA ]] -LRB- as applied to << visual words >> -RRB- to include spatial information in a translation and scale invariant manner .	USED-FOR	[ 10, 10, 15, 16 ]
We develop a new model , << TSI-pLSA >> , which extends pLSA -LRB- as applied to visual words -RRB- to include [[ spatial information ]] in a translation and scale invariant manner .	PART-OF	[ 20, 21, 6, 6 ]
Our [[ approach ]] can handle the high << intra-class variability >> and large proportion of unrelated images returned by search engines .	USED-FOR	[ 1, 1, 6, 7 ]
Our [[ approach ]] can handle the high intra-class variability and large proportion of << unrelated images >> returned by search engines .	USED-FOR	[ 1, 1, 12, 13 ]
Our approach can handle the high [[ intra-class variability ]] and large proportion of << unrelated images >> returned by search engines .	CONJUNCTION	[ 6, 7, 12, 13 ]
Our approach can handle the high intra-class variability and large proportion of << unrelated images >> returned by [[ search engines ]] .	USED-FOR	[ 16, 17, 12, 13 ]
We evaluate the << models >> on standard [[ test sets ]] , showing performance competitive with existing methods trained on hand prepared datasets .	EVALUATE-FOR	[ 6, 7, 3, 3 ]
We evaluate the models on standard [[ test sets ]] , showing performance competitive with existing << methods >> trained on hand prepared datasets .	EVALUATE-FOR	[ 6, 7, 14, 14 ]
We evaluate the << models >> on standard test sets , showing performance competitive with existing [[ methods ]] trained on hand prepared datasets .	COMPARE	[ 14, 14, 3, 3 ]
We evaluate the models on standard test sets , showing performance competitive with existing << methods >> trained on [[ hand prepared datasets ]] .	USED-FOR	[ 17, 19, 14, 14 ]
The paper provides an overview of the research conducted at LIMSI in the field of [[ speech processing ]] , but also in the related areas of << Human-Machine Communication >> , including Natural Language Processing , Non Verbal and Multimodal Communication .	CONJUNCTION	[ 15, 16, 25, 26 ]
The paper provides an overview of the research conducted at LIMSI in the field of speech processing , but also in the related areas of << Human-Machine Communication >> , including [[ Natural Language Processing ]] , Non Verbal and Multimodal Communication .	HYPONYM-OF	[ 29, 31, 25, 26 ]
The paper provides an overview of the research conducted at LIMSI in the field of speech processing , but also in the related areas of Human-Machine Communication , including [[ Natural Language Processing ]] , << Non Verbal and Multimodal Communication >> .	CONJUNCTION	[ 29, 31, 33, 37 ]
The paper provides an overview of the research conducted at LIMSI in the field of speech processing , but also in the related areas of << Human-Machine Communication >> , including Natural Language Processing , [[ Non Verbal and Multimodal Communication ]] .	HYPONYM-OF	[ 33, 37, 25, 26 ]
We have calculated << analytical expressions >> for how the bias and variance of the estimators provided by various temporal difference value estimation algorithms change with offline updates over trials in absorbing Markov chains using [[ lookup table representations ]] .	USED-FOR	[ 33, 35, 3, 4 ]
In this paper , we describe the [[ pronominal anaphora resolution module ]] of << Lucy >> , a portable English understanding system .	PART-OF	[ 7, 10, 12, 12 ]
In this paper , we describe the pronominal anaphora resolution module of [[ Lucy ]] , a portable << English understanding system >> .	HYPONYM-OF	[ 12, 12, 16, 18 ]
In this paper , we reported experiments of << unsupervised automatic acquisition of Italian and English verb subcategorization frames -LRB- SCFs -RRB- >> from [[ general and domain corpora ]] .	USED-FOR	[ 22, 25, 8, 20 ]
The proposed << technique >> operates on [[ syntactically shallow-parsed corpora ]] on the basis of a limited number of search heuristics not relying on any previous lexico-syntactic knowledge about SCFs .	USED-FOR	[ 5, 7, 2, 2 ]
The proposed << technique >> operates on syntactically shallow-parsed corpora on the basis of a limited number of [[ search heuristics ]] not relying on any previous lexico-syntactic knowledge about SCFs .	USED-FOR	[ 16, 17, 2, 2 ]
The proposed technique operates on syntactically shallow-parsed corpora on the basis of a limited number of search heuristics not relying on any previous << lexico-syntactic knowledge >> about [[ SCFs ]] .	FEATURE-OF	[ 26, 26, 23, 24 ]
[[ Graph-cuts optimization ]] is prevalent in << vision and graphics problems >> .	USED-FOR	[ 0, 1, 5, 8 ]
It is thus of great practical importance to parallelize the << graph-cuts optimization >> using to-day 's ubiquitous [[ multi-core machines ]] .	USED-FOR	[ 16, 17, 10, 11 ]
However , the current best << serial algorithm >> by Boykov and Kolmogorov -LSB- 4 -RSB- -LRB- called the [[ BK algorithm ]] -RRB- still has the superior empirical performance .	HYPONYM-OF	[ 17, 18, 5, 6 ]
In this paper , we propose a novel [[ adaptive bottom-up approach ]] to parallelize the << BK algorithm >> .	USED-FOR	[ 8, 10, 14, 15 ]
Extensive experiments in common [[ applications ]] such as 2D/3D image segmentations and 3D surface fitting demonstrate the effectiveness of our << approach >> .	EVALUATE-FOR	[ 4, 4, 19, 19 ]
Extensive experiments in common << applications >> such as [[ 2D/3D image segmentations ]] and 3D surface fitting demonstrate the effectiveness of our approach .	HYPONYM-OF	[ 7, 9, 4, 4 ]
Extensive experiments in common applications such as [[ 2D/3D image segmentations ]] and << 3D surface fitting >> demonstrate the effectiveness of our approach .	CONJUNCTION	[ 7, 9, 11, 13 ]
Extensive experiments in common << applications >> such as 2D/3D image segmentations and [[ 3D surface fitting ]] demonstrate the effectiveness of our approach .	HYPONYM-OF	[ 11, 13, 4, 4 ]
We study the question of how to make loss-aware predictions in image segmentation settings where the << evaluation function >> is the [[ Intersection-over-Union -LRB- IoU -RRB- measure ]] that is used widely in evaluating image segmentation systems .	HYPONYM-OF	[ 20, 24, 16, 17 ]
We study the question of how to make loss-aware predictions in image segmentation settings where the evaluation function is the [[ Intersection-over-Union -LRB- IoU -RRB- measure ]] that is used widely in evaluating << image segmentation systems >> .	EVALUATE-FOR	[ 20, 24, 31, 33 ]
Currently , there are two << dominant approaches >> : the [[ first ]] approximates the Expected-IoU -LRB- EIoU -RRB- score as Expected-Intersection-over-Expected-Union -LRB- EIoEU -RRB- ; and the second approach is to compute exact EIoU but only over a small set of high-quality candidate solutions .	HYPONYM-OF	[ 9, 9, 5, 6 ]
Currently , there are two << dominant approaches >> : the first approximates the Expected-IoU -LRB- EIoU -RRB- score as Expected-Intersection-over-Expected-Union -LRB- EIoEU -RRB- ; and the [[ second approach ]] is to compute exact EIoU but only over a small set of high-quality candidate solutions .	HYPONYM-OF	[ 25, 26, 5, 6 ]
Our new << methods >> use the [[ EIoEU approximation ]] paired with high quality candidate solutions .	USED-FOR	[ 5, 6, 2, 2 ]
Experimentally we show that our new << approaches >> lead to improved performance on both [[ image segmentation tasks ]] .	EVALUATE-FOR	[ 13, 15, 6, 6 ]
Later , however , Breiman cast serious doubt on this explanation by introducing a << boosting algorithm >> , [[ arc-gv ]] , that can generate a higher margins distribution than AdaBoost and yet performs worse .	HYPONYM-OF	[ 17, 17, 14, 15 ]
Later , however , Breiman cast serious doubt on this explanation by introducing a boosting algorithm , [[ arc-gv ]] , that can generate a higher << margins distribution >> than AdaBoost and yet performs worse .	USED-FOR	[ 17, 17, 24, 25 ]
Later , however , Breiman cast serious doubt on this explanation by introducing a boosting algorithm , [[ arc-gv ]] , that can generate a higher margins distribution than << AdaBoost >> and yet performs worse .	COMPARE	[ 17, 17, 27, 27 ]
Although we can reproduce his main finding , we find that the poorer performance of arc-gv can be explained by the increased [[ complexity ]] of the << base classifiers >> it uses , an explanation supported by our experiments and entirely consistent with the margins theory .	EVALUATE-FOR	[ 22, 22, 25, 26 ]
Although we can reproduce his main finding , we find that the poorer performance of << arc-gv >> can be explained by the increased complexity of the [[ base classifiers ]] it uses , an explanation supported by our experiments and entirely consistent with the margins theory .	HYPONYM-OF	[ 25, 26, 15, 15 ]
The [[ transfer phase ]] in << machine translation -LRB- MT -RRB- systems >> has been considered to be more complicated than analysis and generation , since it is inherently a conglomeration of individual lexical rules .	PART-OF	[ 1, 2, 4, 9 ]
The [[ transfer phase ]] in machine translation -LRB- MT -RRB- systems has been considered to be more complicated than << analysis >> and generation , since it is inherently a conglomeration of individual lexical rules .	COMPARE	[ 1, 2, 18, 18 ]
The [[ transfer phase ]] in machine translation -LRB- MT -RRB- systems has been considered to be more complicated than analysis and << generation >> , since it is inherently a conglomeration of individual lexical rules .	COMPARE	[ 1, 2, 20, 20 ]
The transfer phase in machine translation -LRB- MT -RRB- systems has been considered to be more complicated than [[ analysis ]] and << generation >> , since it is inherently a conglomeration of individual lexical rules .	CONJUNCTION	[ 18, 18, 20, 20 ]
Currently some attempts are being made to use [[ case-based reasoning ]] in << machine translation >> , that is , to make decisions on the basis of translation examples at appropriate pints in MT .	USED-FOR	[ 8, 9, 11, 12 ]
This paper proposes a new type of << transfer system >> , called a [[ Similarity-driven Transfer System -LRB- SimTran -RRB- ]] , for use in such case-based MT -LRB- CBMT -RRB- .	HYPONYM-OF	[ 12, 17, 7, 8 ]
This paper proposes a new type of transfer system , called a [[ Similarity-driven Transfer System -LRB- SimTran -RRB- ]] , for use in such << case-based MT -LRB- CBMT -RRB- >> .	USED-FOR	[ 12, 17, 23, 27 ]
This paper addresses the problem of [[ optimal alignment of non-rigid surfaces ]] from multi-view video observations to obtain a << temporally consistent representation >> .	USED-FOR	[ 6, 10, 18, 20 ]
This paper addresses the problem of << optimal alignment of non-rigid surfaces >> from [[ multi-view video observations ]] to obtain a temporally consistent representation .	USED-FOR	[ 12, 14, 6, 10 ]
Conventional << non-rigid surface tracking >> performs [[ frame-to-frame alignment ]] which is subject to the accumulation of errors resulting in a drift over time .	USED-FOR	[ 5, 6, 1, 3 ]
Recently , << non-sequential tracking approaches >> have been introduced which reorder the input data based on a [[ dissimilarity measure ]] .	USED-FOR	[ 16, 17, 2, 4 ]
They demonstrate a reduced drift and increased [[ robustness ]] to large << non-rigid deformations >> .	FEATURE-OF	[ 7, 7, 10, 11 ]
[[ Optimisation of the tree ]] for << non-sequential tracking >> , which minimises the errors in temporal consistency due to both the drift and the jumps , is proposed .	USED-FOR	[ 0, 3, 5, 6 ]
<< Optimisation of the tree >> for non-sequential tracking , which minimises the errors in [[ temporal consistency ]] due to both the drift and the jumps , is proposed .	EVALUATE-FOR	[ 13, 14, 0, 3 ]
A novel [[ cluster tree ]] enforces << sequential tracking in local segments >> of the sequence while allowing global non-sequential traversal among these segments .	USED-FOR	[ 2, 3, 5, 9 ]
A novel [[ cluster tree ]] enforces sequential tracking in local segments of the sequence while allowing << global non-sequential traversal >> among these segments .	USED-FOR	[ 2, 3, 15, 17 ]
Comprehensive evaluation is performed on a variety of challenging << non-rigid surfaces >> including [[ face ]] , cloth and people .	HYPONYM-OF	[ 12, 12, 9, 10 ]
Comprehensive evaluation is performed on a variety of challenging non-rigid surfaces including [[ face ]] , << cloth >> and people .	CONJUNCTION	[ 12, 12, 14, 14 ]
Comprehensive evaluation is performed on a variety of challenging << non-rigid surfaces >> including face , [[ cloth ]] and people .	HYPONYM-OF	[ 14, 14, 9, 10 ]
Comprehensive evaluation is performed on a variety of challenging non-rigid surfaces including face , [[ cloth ]] and << people >> .	CONJUNCTION	[ 14, 14, 16, 16 ]
Comprehensive evaluation is performed on a variety of challenging << non-rigid surfaces >> including face , cloth and [[ people ]] .	HYPONYM-OF	[ 16, 16, 9, 10 ]
It demonstrates that the proposed [[ cluster tree ]] achieves better temporal consistency than the previous << sequential and non-sequential tracking approaches >> .	COMPARE	[ 5, 6, 14, 18 ]
It demonstrates that the proposed << cluster tree >> achieves better [[ temporal consistency ]] than the previous sequential and non-sequential tracking approaches .	EVALUATE-FOR	[ 9, 10, 5, 6 ]
Quantitative analysis on a created [[ synthetic facial performance ]] also shows an improvement by the << cluster tree >> .	EVALUATE-FOR	[ 5, 7, 14, 15 ]
The << translation of English text into American Sign Language -LRB- ASL -RRB- animation >> tests the limits of traditional [[ MT architectural designs ]] .	USED-FOR	[ 18, 20, 1, 12 ]
A new [[ semantic representation ]] is proposed that uses virtual reality 3D scene modeling software to produce << spatially complex ASL phenomena >> called '' classifier predicates . ''	USED-FOR	[ 2, 3, 16, 19 ]
A new << semantic representation >> is proposed that uses [[ virtual reality 3D scene modeling software ]] to produce spatially complex ASL phenomena called '' classifier predicates . ''	USED-FOR	[ 8, 13, 2, 3 ]
A new semantic representation is proposed that uses virtual reality 3D scene modeling software to produce << spatially complex ASL phenomena >> called '' [[ classifier predicates ]] . ''	HYPONYM-OF	[ 22, 23, 16, 19 ]
The model acts as an interlingua within a new multi-pathway MT architecture design that also incorporates [[ transfer ]] and << direct approaches >> into a single system .	CONJUNCTION	[ 16, 16, 18, 19 ]
The model acts as an interlingua within a new multi-pathway MT architecture design that also incorporates [[ transfer ]] and direct approaches into a single << system >> .	PART-OF	[ 16, 16, 23, 23 ]
The model acts as an interlingua within a new multi-pathway MT architecture design that also incorporates transfer and [[ direct approaches ]] into a single << system >> .	PART-OF	[ 18, 19, 23, 23 ]
An << extension >> to the [[ GPSG grammatical formalism ]] is proposed , allowing non-terminals to consist of finite sequences of category labels , and allowing schematic variables to range over such sequences .	USED-FOR	[ 4, 6, 1, 1 ]
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 .	USED-FOR	[ 1, 1, 12, 12 ]

[[ Honorifics ]] are used extensively in << Japanese >> , reflecting the social relationship -LRB- e.g. social ranks and age -RRB- of the referents .

USED-FOR

[ 0, 0, 5, 5 ]

This [[ referential information ]] is vital for resolving << zero pronouns >> and improving machine translation outputs .

USED-FOR

[ 1, 2, 7, 8 ]

This [[ referential information ]] is vital for resolving zero pronouns and improving << machine translation outputs >> .

USED-FOR

[ 1, 2, 11, 13 ]

<< Visually-guided arm reaching movements >> are produced by [[ distributed neural networks ]] within parietal and frontal regions of the cerebral cortex .

USED-FOR

[ 7, 9, 0, 3 ]

Experimental data indicate that -LRB- I -RRB- single neurons in these regions are broadly tuned to parameters of movement ; -LRB- 2 -RRB- appropriate commands are elaborated by populations of neurons ; -LRB- 3 -RRB- the << coordinated action of neu-rons >> can be visualized using a [[ neuronal population vector -LRB- NPV -RRB- ]] .

USED-FOR

[ 45, 50, 36, 39 ]

We designed a [[ model ]] of the << cortical motor command >> to investigate the relation between the desired direction of the movement , the actual direction of movement and the direction of the NPV in motor cortex .

USED-FOR

[ 3, 3, 6, 8 ]

We designed a model of the cortical motor command to investigate the relation between the desired direction of the movement , the actual direction of movement and the direction of the [[ NPV ]] in << motor cortex >> .

USED-FOR

[ 31, 31, 33, 34 ]

The model is a [[ two-layer self-organizing neural network ]] which combines broadly-tuned -LRB- muscular -RRB- proprioceptive and -LRB- cartesian -RRB- visual information to calculate << -LRB- angular -RRB- motor commands >> for the initial part of the movement of a two-link arm .

USED-FOR

[ 4, 7, 23, 27 ]

The model is a << two-layer self-organizing neural network >> which combines [[ broadly-tuned -LRB- muscular -RRB- proprioceptive ]] and -LRB- cartesian -RRB- visual information to calculate -LRB- angular -RRB- motor commands for the initial part of the movement of a two-link arm .

USED-FOR

[ 10, 14, 4, 7 ]

The model is a two-layer self-organizing neural network which combines [[ broadly-tuned -LRB- muscular -RRB- proprioceptive ]] and << -LRB- cartesian -RRB- visual information >> to calculate -LRB- angular -RRB- motor commands for the initial part of the movement of a two-link arm .

CONJUNCTION

[ 10, 14, 16, 20 ]

The model is a << two-layer self-organizing neural network >> which combines broadly-tuned -LRB- muscular -RRB- proprioceptive and [[ -LRB- cartesian -RRB- visual information ]] to calculate -LRB- angular -RRB- motor commands for the initial part of the movement of a two-link arm .

USED-FOR

[ 16, 20, 4, 7 ]

These results suggest the NPV does not give a faithful << image of cortical processing >> during [[ arm reaching movements ]] .

FEATURE-OF

[ 15, 17, 10, 13 ]

It is well-known that diversity among [[ base classifiers ]] is crucial for constructing a strong << ensemble >> .

USED-FOR

[ 6, 7, 14, 14 ]

In this paper , we propose an alternative way for << ensemble construction >> by [[ resampling pairwise constraints ]] that specify whether a pair of instances belongs to the same class or not .

USED-FOR

[ 13, 15, 10, 11 ]

Using [[ pairwise constraints ]] for << ensemble construction >> is challenging because it remains unknown how to influence the base classifiers with the sampled pairwise constraints .

USED-FOR

[ 1, 2, 4, 5 ]

First , we transform the original instances into a new << data representation >> using [[ projections ]] learnt from pairwise constraints .

USED-FOR

[ 13, 13, 10, 11 ]

First , we transform the original instances into a new data representation using << projections >> learnt from [[ pairwise constraints ]] .

USED-FOR

[ 16, 17, 13, 13 ]

Then , we build the << base clas-sifiers >> with the new [[ data representation ]] .

USED-FOR

[ 10, 11, 5, 6 ]

We propose two methods for << resampling pairwise constraints >> following the standard [[ Bagging and Boosting algorithms ]] , respectively .

USED-FOR

[ 11, 14, 5, 7 ]

A new [[ algorithm ]] for solving the three << dimensional container packing problem >> is proposed in this paper .

USED-FOR

[ 2, 2, 7, 10 ]

This new [[ algorithm ]] deviates from the traditional << approach of wall building and layering >> .

COMPARE

[ 2, 2, 7, 12 ]

We tested our << method >> using all 760 test cases from the [[ OR-Library ]] .

EVALUATE-FOR

[ 11, 11, 3, 3 ]

Experimental results indicate that the new << algorithm >> is able to achieve an [[ average packing utilization ]] of more than 87 % .

EVALUATE-FOR

[ 12, 14, 6, 6 ]

Current [[ approaches ]] to << object category recognition >> require datasets of training images to be manually prepared , with varying degrees of supervision .

USED-FOR

[ 1, 1, 3, 5 ]

Current << approaches >> to object category recognition require [[ datasets ]] of training images to be manually prepared , with varying degrees of supervision .

USED-FOR

[ 7, 7, 1, 1 ]

We present an [[ approach ]] that can learn an << object category >> from just its name , by utilizing the raw output of image search engines available on the Internet .

USED-FOR

[ 3, 3, 8, 9 ]

We develop a new model , << TSI-pLSA >> , which extends [[ pLSA ]] -LRB- as applied to visual words -RRB- to include spatial information in a translation and scale invariant manner .

USED-FOR

[ 10, 10, 6, 6 ]

We develop a new model , TSI-pLSA , which extends [[ pLSA ]] -LRB- as applied to << visual words >> -RRB- to include spatial information in a translation and scale invariant manner .

USED-FOR

[ 10, 10, 15, 16 ]

We develop a new model , << TSI-pLSA >> , which extends pLSA -LRB- as applied to visual words -RRB- to include [[ spatial information ]] in a translation and scale invariant manner .

PART-OF

[ 20, 21, 6, 6 ]

Our [[ approach ]] can handle the high << intra-class variability >> and large proportion of unrelated images returned by search engines .

USED-FOR

[ 1, 1, 6, 7 ]

Our [[ approach ]] can handle the high intra-class variability and large proportion of << unrelated images >> returned by search engines .

USED-FOR

[ 1, 1, 12, 13 ]

Our approach can handle the high [[ intra-class variability ]] and large proportion of << unrelated images >> returned by search engines .

CONJUNCTION

[ 6, 7, 12, 13 ]

Our approach can handle the high intra-class variability and large proportion of << unrelated images >> returned by [[ search engines ]] .

USED-FOR

[ 16, 17, 12, 13 ]

We evaluate the << models >> on standard [[ test sets ]] , showing performance competitive with existing methods trained on hand prepared datasets .

EVALUATE-FOR

[ 6, 7, 3, 3 ]

We evaluate the models on standard [[ test sets ]] , showing performance competitive with existing << methods >> trained on hand prepared datasets .

EVALUATE-FOR

[ 6, 7, 14, 14 ]

We evaluate the << models >> on standard test sets , showing performance competitive with existing [[ methods ]] trained on hand prepared datasets .

COMPARE

[ 14, 14, 3, 3 ]

We evaluate the models on standard test sets , showing performance competitive with existing << methods >> trained on [[ hand prepared datasets ]] .

USED-FOR

[ 17, 19, 14, 14 ]

The paper provides an overview of the research conducted at LIMSI in the field of [[ speech processing ]] , but also in the related areas of << Human-Machine Communication >> , including Natural Language Processing , Non Verbal and Multimodal Communication .

CONJUNCTION

[ 15, 16, 25, 26 ]

The paper provides an overview of the research conducted at LIMSI in the field of speech processing , but also in the related areas of << Human-Machine Communication >> , including [[ Natural Language Processing ]] , Non Verbal and Multimodal Communication .

HYPONYM-OF

[ 29, 31, 25, 26 ]

The paper provides an overview of the research conducted at LIMSI in the field of speech processing , but also in the related areas of Human-Machine Communication , including [[ Natural Language Processing ]] , << Non Verbal and Multimodal Communication >> .

CONJUNCTION

[ 29, 31, 33, 37 ]

The paper provides an overview of the research conducted at LIMSI in the field of speech processing , but also in the related areas of << Human-Machine Communication >> , including Natural Language Processing , [[ Non Verbal and Multimodal Communication ]] .

HYPONYM-OF

[ 33, 37, 25, 26 ]

We have calculated << analytical expressions >> for how the bias and variance of the estimators provided by various temporal difference value estimation algorithms change with offline updates over trials in absorbing Markov chains using [[ lookup table representations ]] .

USED-FOR

[ 33, 35, 3, 4 ]

In this paper , we describe the [[ pronominal anaphora resolution module ]] of << Lucy >> , a portable English understanding system .

PART-OF

[ 7, 10, 12, 12 ]

In this paper , we describe the pronominal anaphora resolution module of [[ Lucy ]] , a portable << English understanding system >> .

HYPONYM-OF

[ 12, 12, 16, 18 ]

In this paper , we reported experiments of << unsupervised automatic acquisition of Italian and English verb subcategorization frames -LRB- SCFs -RRB- >> from [[ general and domain corpora ]] .

USED-FOR

[ 22, 25, 8, 20 ]

The proposed << technique >> operates on [[ syntactically shallow-parsed corpora ]] on the basis of a limited number of search heuristics not relying on any previous lexico-syntactic knowledge about SCFs .

USED-FOR

[ 5, 7, 2, 2 ]

The proposed << technique >> operates on syntactically shallow-parsed corpora on the basis of a limited number of [[ search heuristics ]] not relying on any previous lexico-syntactic knowledge about SCFs .

USED-FOR

[ 16, 17, 2, 2 ]

The proposed technique operates on syntactically shallow-parsed corpora on the basis of a limited number of search heuristics not relying on any previous << lexico-syntactic knowledge >> about [[ SCFs ]] .

FEATURE-OF

[ 26, 26, 23, 24 ]

[[ Graph-cuts optimization ]] is prevalent in << vision and graphics problems >> .

USED-FOR

[ 0, 1, 5, 8 ]

It is thus of great practical importance to parallelize the << graph-cuts optimization >> using to-day 's ubiquitous [[ multi-core machines ]] .

USED-FOR

[ 16, 17, 10, 11 ]

However , the current best << serial algorithm >> by Boykov and Kolmogorov -LSB- 4 -RSB- -LRB- called the [[ BK algorithm ]] -RRB- still has the superior empirical performance .

HYPONYM-OF

[ 17, 18, 5, 6 ]

In this paper , we propose a novel [[ adaptive bottom-up approach ]] to parallelize the << BK algorithm >> .

USED-FOR

[ 8, 10, 14, 15 ]

Extensive experiments in common [[ applications ]] such as 2D/3D image segmentations and 3D surface fitting demonstrate the effectiveness of our << approach >> .

EVALUATE-FOR

[ 4, 4, 19, 19 ]

Extensive experiments in common << applications >> such as [[ 2D/3D image segmentations ]] and 3D surface fitting demonstrate the effectiveness of our approach .

HYPONYM-OF

[ 7, 9, 4, 4 ]

Extensive experiments in common applications such as [[ 2D/3D image segmentations ]] and << 3D surface fitting >> demonstrate the effectiveness of our approach .

CONJUNCTION

[ 7, 9, 11, 13 ]

Extensive experiments in common << applications >> such as 2D/3D image segmentations and [[ 3D surface fitting ]] demonstrate the effectiveness of our approach .

HYPONYM-OF

[ 11, 13, 4, 4 ]

We study the question of how to make loss-aware predictions in image segmentation settings where the << evaluation function >> is the [[ Intersection-over-Union -LRB- IoU -RRB- measure ]] that is used widely in evaluating image segmentation systems .

HYPONYM-OF

[ 20, 24, 16, 17 ]

We study the question of how to make loss-aware predictions in image segmentation settings where the evaluation function is the [[ Intersection-over-Union -LRB- IoU -RRB- measure ]] that is used widely in evaluating << image segmentation systems >> .

EVALUATE-FOR

[ 20, 24, 31, 33 ]

Currently , there are two << dominant approaches >> : the [[ first ]] approximates the Expected-IoU -LRB- EIoU -RRB- score as Expected-Intersection-over-Expected-Union -LRB- EIoEU -RRB- ; and the second approach is to compute exact EIoU but only over a small set of high-quality candidate solutions .

HYPONYM-OF

[ 9, 9, 5, 6 ]

Currently , there are two << dominant approaches >> : the first approximates the Expected-IoU -LRB- EIoU -RRB- score as Expected-Intersection-over-Expected-Union -LRB- EIoEU -RRB- ; and the [[ second approach ]] is to compute exact EIoU but only over a small set of high-quality candidate solutions .

HYPONYM-OF

[ 25, 26, 5, 6 ]

Our new << methods >> use the [[ EIoEU approximation ]] paired with high quality candidate solutions .

USED-FOR

[ 5, 6, 2, 2 ]

Experimentally we show that our new << approaches >> lead to improved performance on both [[ image segmentation tasks ]] .

EVALUATE-FOR

[ 13, 15, 6, 6 ]

Later , however , Breiman cast serious doubt on this explanation by introducing a << boosting algorithm >> , [[ arc-gv ]] , that can generate a higher margins distribution than AdaBoost and yet performs worse .

HYPONYM-OF

[ 17, 17, 14, 15 ]

Later , however , Breiman cast serious doubt on this explanation by introducing a boosting algorithm , [[ arc-gv ]] , that can generate a higher << margins distribution >> than AdaBoost and yet performs worse .

USED-FOR

[ 17, 17, 24, 25 ]

Later , however , Breiman cast serious doubt on this explanation by introducing a boosting algorithm , [[ arc-gv ]] , that can generate a higher margins distribution than << AdaBoost >> and yet performs worse .

COMPARE

[ 17, 17, 27, 27 ]

Although we can reproduce his main finding , we find that the poorer performance of arc-gv can be explained by the increased [[ complexity ]] of the << base classifiers >> it uses , an explanation supported by our experiments and entirely consistent with the margins theory .

EVALUATE-FOR

[ 22, 22, 25, 26 ]

Although we can reproduce his main finding , we find that the poorer performance of << arc-gv >> can be explained by the increased complexity of the [[ base classifiers ]] it uses , an explanation supported by our experiments and entirely consistent with the margins theory .

HYPONYM-OF

[ 25, 26, 15, 15 ]

The [[ transfer phase ]] in << machine translation -LRB- MT -RRB- systems >> has been considered to be more complicated than analysis and generation , since it is inherently a conglomeration of individual lexical rules .

PART-OF

[ 1, 2, 4, 9 ]

The [[ transfer phase ]] in machine translation -LRB- MT -RRB- systems has been considered to be more complicated than << analysis >> and generation , since it is inherently a conglomeration of individual lexical rules .

COMPARE

[ 1, 2, 18, 18 ]

The [[ transfer phase ]] in machine translation -LRB- MT -RRB- systems has been considered to be more complicated than analysis and << generation >> , since it is inherently a conglomeration of individual lexical rules .

COMPARE

[ 1, 2, 20, 20 ]

The transfer phase in machine translation -LRB- MT -RRB- systems has been considered to be more complicated than [[ analysis ]] and << generation >> , since it is inherently a conglomeration of individual lexical rules .

CONJUNCTION

[ 18, 18, 20, 20 ]

Currently some attempts are being made to use [[ case-based reasoning ]] in << machine translation >> , that is , to make decisions on the basis of translation examples at appropriate pints in MT .

USED-FOR

[ 8, 9, 11, 12 ]

This paper proposes a new type of << transfer system >> , called a [[ Similarity-driven Transfer System -LRB- SimTran -RRB- ]] , for use in such case-based MT -LRB- CBMT -RRB- .

HYPONYM-OF

[ 12, 17, 7, 8 ]

This paper proposes a new type of transfer system , called a [[ Similarity-driven Transfer System -LRB- SimTran -RRB- ]] , for use in such << case-based MT -LRB- CBMT -RRB- >> .

USED-FOR

[ 12, 17, 23, 27 ]

This paper addresses the problem of [[ optimal alignment of non-rigid surfaces ]] from multi-view video observations to obtain a << temporally consistent representation >> .

USED-FOR

[ 6, 10, 18, 20 ]

This paper addresses the problem of << optimal alignment of non-rigid surfaces >> from [[ multi-view video observations ]] to obtain a temporally consistent representation .

USED-FOR

[ 12, 14, 6, 10 ]

Conventional << non-rigid surface tracking >> performs [[ frame-to-frame alignment ]] which is subject to the accumulation of errors resulting in a drift over time .

USED-FOR

[ 5, 6, 1, 3 ]

Recently , << non-sequential tracking approaches >> have been introduced which reorder the input data based on a [[ dissimilarity measure ]] .

USED-FOR

[ 16, 17, 2, 4 ]

They demonstrate a reduced drift and increased [[ robustness ]] to large << non-rigid deformations >> .

FEATURE-OF

[ 7, 7, 10, 11 ]

[[ Optimisation of the tree ]] for << non-sequential tracking >> , which minimises the errors in temporal consistency due to both the drift and the jumps , is proposed .

USED-FOR

[ 0, 3, 5, 6 ]

<< Optimisation of the tree >> for non-sequential tracking , which minimises the errors in [[ temporal consistency ]] due to both the drift and the jumps , is proposed .

EVALUATE-FOR

[ 13, 14, 0, 3 ]

A novel [[ cluster tree ]] enforces << sequential tracking in local segments >> of the sequence while allowing global non-sequential traversal among these segments .

USED-FOR

[ 2, 3, 5, 9 ]

A novel [[ cluster tree ]] enforces sequential tracking in local segments of the sequence while allowing << global non-sequential traversal >> among these segments .

USED-FOR

[ 2, 3, 15, 17 ]

Comprehensive evaluation is performed on a variety of challenging << non-rigid surfaces >> including [[ face ]] , cloth and people .

HYPONYM-OF

[ 12, 12, 9, 10 ]

Comprehensive evaluation is performed on a variety of challenging non-rigid surfaces including [[ face ]] , << cloth >> and people .

CONJUNCTION

[ 12, 12, 14, 14 ]

Comprehensive evaluation is performed on a variety of challenging << non-rigid surfaces >> including face , [[ cloth ]] and people .

HYPONYM-OF

[ 14, 14, 9, 10 ]

Comprehensive evaluation is performed on a variety of challenging non-rigid surfaces including face , [[ cloth ]] and << people >> .

CONJUNCTION

[ 14, 14, 16, 16 ]

Comprehensive evaluation is performed on a variety of challenging << non-rigid surfaces >> including face , cloth and [[ people ]] .

HYPONYM-OF

[ 16, 16, 9, 10 ]

It demonstrates that the proposed [[ cluster tree ]] achieves better temporal consistency than the previous << sequential and non-sequential tracking approaches >> .

COMPARE

[ 5, 6, 14, 18 ]

It demonstrates that the proposed << cluster tree >> achieves better [[ temporal consistency ]] than the previous sequential and non-sequential tracking approaches .

EVALUATE-FOR

[ 9, 10, 5, 6 ]

Quantitative analysis on a created [[ synthetic facial performance ]] also shows an improvement by the << cluster tree >> .

EVALUATE-FOR

[ 5, 7, 14, 15 ]

The << translation of English text into American Sign Language -LRB- ASL -RRB- animation >> tests the limits of traditional [[ MT architectural designs ]] .

USED-FOR

[ 18, 20, 1, 12 ]

A new [[ semantic representation ]] is proposed that uses virtual reality 3D scene modeling software to produce << spatially complex ASL phenomena >> called '' classifier predicates . ''

USED-FOR

[ 2, 3, 16, 19 ]

A new << semantic representation >> is proposed that uses [[ virtual reality 3D scene modeling software ]] to produce spatially complex ASL phenomena called '' classifier predicates . ''

USED-FOR

[ 8, 13, 2, 3 ]

A new semantic representation is proposed that uses virtual reality 3D scene modeling software to produce << spatially complex ASL phenomena >> called '' [[ classifier predicates ]] . ''

HYPONYM-OF

[ 22, 23, 16, 19 ]

The model acts as an interlingua within a new multi-pathway MT architecture design that also incorporates [[ transfer ]] and << direct approaches >> into a single system .

CONJUNCTION

[ 16, 16, 18, 19 ]

The model acts as an interlingua within a new multi-pathway MT architecture design that also incorporates [[ transfer ]] and direct approaches into a single << system >> .

PART-OF

[ 16, 16, 23, 23 ]

The model acts as an interlingua within a new multi-pathway MT architecture design that also incorporates transfer and [[ direct approaches ]] into a single << system >> .

PART-OF

[ 18, 19, 23, 23 ]

An << extension >> to the [[ GPSG grammatical formalism ]] is proposed , allowing non-terminals to consist of finite sequences of category labels , and allowing schematic variables to range over such sequences .

USED-FOR

[ 4, 6, 1, 1 ]

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 .

USED-FOR

[ 1, 1, 12, 12 ]