text stringlengths 49 577 | label stringclasses 7
values | metadata list |
|---|---|---|
Finally , a [[ cross-corpus -LRB- and cross-language -RRB- experiment ]] reveals better noise and reverberation robustness for DOCCs than for << MFCCs >> . | EVALUATE-FOR | [
3,
8,
19,
19
] |
Finally , a cross-corpus -LRB- and cross-language -RRB- experiment reveals better [[ noise and reverberation robustness ]] for << DOCCs >> than for MFCCs . | EVALUATE-FOR | [
11,
14,
16,
16
] |
Finally , a cross-corpus -LRB- and cross-language -RRB- experiment reveals better [[ noise and reverberation robustness ]] for DOCCs than for << MFCCs >> . | EVALUATE-FOR | [
11,
14,
19,
19
] |
Finally , a cross-corpus -LRB- and cross-language -RRB- experiment reveals better noise and reverberation robustness for [[ DOCCs ]] than for << MFCCs >> . | COMPARE | [
16,
16,
19,
19
] |
This paper proposes [[ document oriented preference sets -LRB- DoPS -RRB- ]] for the << disambiguation of the dependency structure >> of sentences . | USED-FOR | [
3,
9,
12,
16
] |
<< Sentence ambiguities >> can be resolved by using [[ domain targeted preference knowledge ]] without using complicated large knowledgebases . | USED-FOR | [
7,
10,
0,
1
] |
Sentence ambiguities can be resolved by using [[ domain targeted preference knowledge ]] without using complicated large << knowledgebases >> . | COMPARE | [
7,
10,
15,
15
] |
Implementation and empirical results are described for the the analysis of [[ dependency structures ]] of << Japanese patent claim sentences >> . | FEATURE-OF | [
11,
12,
14,
17
] |
<< Multimodal interfaces >> require effective [[ parsing ]] and understanding of utterances whose content is distributed across multiple input modes . | USED-FOR | [
4,
4,
0,
1
] |
Johnston 1998 presents an [[ approach ]] in which strategies for << multimodal integration >> are stated declaratively using a unification-based grammar that is used by a multidimensional chart parser to compose inputs . | USED-FOR | [
4,
4,
9,
10
] |
Johnston 1998 presents an approach in which strategies for << multimodal integration >> are stated declaratively using a [[ unification-based grammar ]] that is used by a multidimensional chart parser to compose inputs . | USED-FOR | [
16,
17,
9,
10
] |
Johnston 1998 presents an approach in which strategies for multimodal integration are stated declaratively using a [[ unification-based grammar ]] that is used by a << multidimensional chart parser >> to compose inputs . | USED-FOR | [
16,
17,
23,
25
] |
In this paper , we present an alternative [[ approach ]] in which << multimodal parsing and understanding >> are achieved using a weighted finite-state device which takes speech and gesture streams as inputs and outputs their joint interpretation . | USED-FOR | [
8,
8,
11,
14
] |
In this paper , we present an alternative approach in which << multimodal parsing and understanding >> are achieved using a [[ weighted finite-state device ]] which takes speech and gesture streams as inputs and outputs their joint interpretation . | USED-FOR | [
19,
21,
11,
14
] |
In this paper , we present an alternative approach in which multimodal parsing and understanding are achieved using a << weighted finite-state device >> which takes [[ speech and gesture streams ]] as inputs and outputs their joint interpretation . | USED-FOR | [
24,
27,
19,
21
] |
This [[ approach ]] is significantly more efficient , enables tight-coupling of multimodal understanding with speech recognition , and provides a general probabilistic framework for << multimodal ambiguity resolution >> . | USED-FOR | [
1,
1,
23,
25
] |
This approach is significantly more efficient , enables tight-coupling of << multimodal understanding >> with [[ speech recognition ]] , and provides a general probabilistic framework for multimodal ambiguity resolution . | CONJUNCTION | [
13,
14,
10,
11
] |
Recently , we initiated a project to develop a << phonetically-based spoken language understanding system >> called [[ SUMMIT ]] . | HYPONYM-OF | [
15,
15,
9,
13
] |
In contrast to many of the past efforts that make use of << heuristic rules >> whose development requires intense [[ knowledge engineering ]] , our approach attempts to express the speech knowledge within a formal framework using well-defined mathematical tools . | USED-FOR | [
18,
19,
12,
13
] |
In contrast to many of the past efforts that make use of heuristic rules whose development requires intense knowledge engineering , our [[ approach ]] attempts to express the << speech knowledge >> within a formal framework using well-defined mathematical tools . | USED-FOR | [
22,
22,
27,
28
] |
In contrast to many of the past efforts that make use of heuristic rules whose development requires intense knowledge engineering , our approach attempts to express the << speech knowledge >> within a formal framework using well-defined [[ mathematical tools ]] . | USED-FOR | [
35,
36,
27,
28
] |
In our system , [[ features ]] and << decision strategies >> are discovered and trained automatically , using a large body of speech data . | CONJUNCTION | [
4,
4,
6,
7
] |
In our system , features and << decision strategies >> are discovered and trained automatically , using a large body of [[ speech data ]] . | USED-FOR | [
19,
20,
6,
7
] |
This paper describes an implemented << program >> that takes a [[ tagged text corpus ]] and generates a partial list of the subcategorization frames in which each verb occurs . | EVALUATE-FOR | [
9,
11,
5,
5
] |
We present a [[ method ]] for estimating the << relative pose of two calibrated or uncalibrated non-overlapping surveillance cameras >> from observing a moving object . | USED-FOR | [
3,
3,
7,
16
] |
We show how to tackle the problem of << missing point correspondences >> heavily required by [[ SfM pipelines ]] and how to go beyond this basic paradigm . | USED-FOR | [
14,
15,
8,
10
] |
We relax the [[ non-linear nature ]] of the << problem >> by accepting two assumptions which surveillance scenarios offer , ie . | FEATURE-OF | [
3,
4,
7,
7
] |
By those assumptions we cast the << problem >> as a [[ Quadratic Eigenvalue Problem ]] offering an elegant way of treating nonlinear monomials and delivering a quasi closed-form solution as a reliable starting point for a further bundle adjustment . | USED-FOR | [
9,
11,
6,
6
] |
By those assumptions we cast the problem as a [[ Quadratic Eigenvalue Problem ]] offering an elegant way of treating << nonlinear monomials >> and delivering a quasi closed-form solution as a reliable starting point for a further bundle adjustment . | USED-FOR | [
9,
11,
18,
19
] |
By those assumptions we cast the problem as a [[ Quadratic Eigenvalue Problem ]] offering an elegant way of treating nonlinear monomials and delivering a << quasi closed-form solution >> as a reliable starting point for a further bundle adjustment . | USED-FOR | [
9,
11,
23,
25
] |
By those assumptions we cast the problem as a Quadratic Eigenvalue Problem offering an elegant way of treating nonlinear monomials and delivering a [[ quasi closed-form solution ]] as a reliable starting point for a further << bundle adjustment >> . | USED-FOR | [
23,
25,
34,
35
] |
We are the first to bring the [[ closed form solution ]] to such a very practical << problem >> arising in video surveillance . | USED-FOR | [
7,
9,
15,
15
] |
We are the first to bring the closed form solution to such a very practical << problem >> arising in [[ video surveillance ]] . | FEATURE-OF | [
18,
19,
15,
15
] |
In this paper , we propose a [[ human action recognition system ]] suitable for << embedded computer vision applications >> in security systems , human-computer interaction and intelligent environments . | USED-FOR | [
7,
10,
13,
16
] |
In this paper , we propose a human action recognition system suitable for [[ embedded computer vision applications ]] in << security systems >> , human-computer interaction and intelligent environments . | USED-FOR | [
13,
16,
18,
19
] |
In this paper , we propose a human action recognition system suitable for [[ embedded computer vision applications ]] in security systems , << human-computer interaction >> and intelligent environments . | USED-FOR | [
13,
16,
21,
22
] |
In this paper , we propose a human action recognition system suitable for [[ embedded computer vision applications ]] in security systems , human-computer interaction and << intelligent environments >> . | USED-FOR | [
13,
16,
24,
25
] |
In this paper , we propose a human action recognition system suitable for embedded computer vision applications in [[ security systems ]] , << human-computer interaction >> and intelligent environments . | CONJUNCTION | [
18,
19,
21,
22
] |
In this paper , we propose a human action recognition system suitable for embedded computer vision applications in security systems , [[ human-computer interaction ]] and << intelligent environments >> . | CONJUNCTION | [
21,
22,
24,
25
] |
Our [[ system ]] is suitable for << embedded computer vision application >> based on three reasons . | USED-FOR | [
1,
1,
5,
8
] |
Firstly , the << system >> was based on a [[ linear Support Vector Machine -LRB- SVM -RRB- classifier ]] where classification progress can be implemented easily and quickly in embedded hardware . | USED-FOR | [
8,
15,
3,
3
] |
Firstly , the system was based on a linear Support Vector Machine -LRB- SVM -RRB- classifier where << classification progress >> can be implemented easily and quickly in [[ embedded hardware ]] . | USED-FOR | [
26,
27,
17,
18
] |
Secondly , we use << compacted motion features >> easily obtained from [[ videos ]] . | USED-FOR | [
10,
10,
4,
6
] |
We address the limitations of the well known Motion History Image -LRB- MHI -RRB- and propose a new [[ Hierarchical Motion History Histogram -LRB- HMHH -RRB- feature ]] to represent the << motion information >> . | USED-FOR | [
18,
25,
29,
30
] |
[[ HMHH ]] not only provides << rich motion information >> , but also remains computationally inexpensive . | USED-FOR | [
0,
0,
4,
6
] |
Finally , we combine [[ MHI ]] and << HMHH >> together and extract a low dimension feature vector to be used in the SVM classifiers . | CONJUNCTION | [
4,
4,
6,
6
] |
Finally , we combine [[ MHI ]] and HMHH together and extract a << low dimension feature vector >> to be used in the SVM classifiers . | USED-FOR | [
4,
4,
11,
14
] |
Finally , we combine MHI and [[ HMHH ]] together and extract a << low dimension feature vector >> to be used in the SVM classifiers . | USED-FOR | [
6,
6,
11,
14
] |
Finally , we combine MHI and HMHH together and extract a [[ low dimension feature vector ]] to be used in the << SVM classifiers >> . | USED-FOR | [
11,
14,
20,
21
] |
Experimental results show that our << system >> achieves significant improvement on the [[ recognition ]] performance . | EVALUATE-FOR | [
11,
11,
5,
5
] |
In this paper I will argue for a << model of grammatical processing >> that is based on [[ uniform processing ]] and knowledge sources . | USED-FOR | [
16,
17,
8,
11
] |
In this paper I will argue for a << model of grammatical processing >> that is based on uniform processing and [[ knowledge sources ]] . | USED-FOR | [
19,
20,
8,
11
] |
In this paper I will argue for a model of grammatical processing that is based on << uniform processing >> and [[ knowledge sources ]] . | CONJUNCTION | [
19,
20,
16,
17
] |
The main feature of this model is to view [[ parsing ]] and << generation >> as two strongly interleaved tasks performed by a single parametrized deduction process . | CONJUNCTION | [
9,
9,
11,
11
] |
The main feature of this model is to view [[ parsing ]] and generation as two strongly interleaved << tasks >> performed by a single parametrized deduction process . | HYPONYM-OF | [
9,
9,
16,
16
] |
The main feature of this model is to view parsing and [[ generation ]] as two strongly interleaved << tasks >> performed by a single parametrized deduction process . | HYPONYM-OF | [
11,
11,
16,
16
] |
The main feature of this model is to view parsing and generation as two strongly interleaved << tasks >> performed by a single [[ parametrized deduction process ]] . | USED-FOR | [
21,
23,
16,
16
] |
[[ Link detection ]] has been regarded as a core technology for the << Topic Detection and Tracking tasks of new event detection >> . | USED-FOR | [
0,
1,
11,
19
] |
In this paper we formulate [[ story link detection ]] and << new event detection >> as information retrieval task and hypothesize on the impact of precision and recall on both systems . | CONJUNCTION | [
5,
7,
9,
11
] |
In this paper we formulate [[ story link detection ]] and new event detection as information retrieval task and hypothesize on the impact of precision and recall on both << systems >> . | HYPONYM-OF | [
5,
7,
27,
27
] |
In this paper we formulate story link detection and [[ new event detection ]] as information retrieval task and hypothesize on the impact of precision and recall on both << systems >> . | HYPONYM-OF | [
9,
11,
27,
27
] |
In this paper we formulate << story link detection >> and new event detection as [[ information retrieval task ]] and hypothesize on the impact of precision and recall on both systems . | USED-FOR | [
13,
15,
5,
7
] |
In this paper we formulate story link detection and << new event detection >> as [[ information retrieval task ]] and hypothesize on the impact of precision and recall on both systems . | USED-FOR | [
13,
15,
9,
11
] |
In this paper we formulate story link detection and new event detection as information retrieval task and hypothesize on the impact of [[ precision ]] and << recall >> on both systems . | CONJUNCTION | [
22,
22,
24,
24
] |
In this paper we formulate story link detection and new event detection as information retrieval task and hypothesize on the impact of [[ precision ]] and recall on both << systems >> . | EVALUATE-FOR | [
22,
22,
27,
27
] |
In this paper we formulate story link detection and new event detection as information retrieval task and hypothesize on the impact of precision and [[ recall ]] on both << systems >> . | EVALUATE-FOR | [
24,
24,
27,
27
] |
Motivated by these arguments , we introduce a number of new << performance enhancing techniques >> including [[ part of speech tagging ]] , new similarity measures and expanded stop lists . | PART-OF | [
15,
18,
11,
13
] |
Motivated by these arguments , we introduce a number of new performance enhancing techniques including [[ part of speech tagging ]] , new << similarity measures >> and expanded stop lists . | CONJUNCTION | [
15,
18,
21,
22
] |
Motivated by these arguments , we introduce a number of new << performance enhancing techniques >> including part of speech tagging , new [[ similarity measures ]] and expanded stop lists . | PART-OF | [
21,
22,
11,
13
] |
Motivated by these arguments , we introduce a number of new performance enhancing techniques including part of speech tagging , new [[ similarity measures ]] and << expanded stop lists >> . | CONJUNCTION | [
21,
22,
24,
26
] |
Motivated by these arguments , we introduce a number of new << performance enhancing techniques >> including part of speech tagging , new similarity measures and [[ expanded stop lists ]] . | PART-OF | [
24,
26,
11,
13
] |
We attempt to understand << visual classification >> in humans using both [[ psy-chophysical and machine learning techniques ]] . | USED-FOR | [
10,
14,
4,
5
] |
[[ Frontal views of human faces ]] were used for a << gender classification task >> . | USED-FOR | [
0,
4,
9,
11
] |
Several [[ hyperplane learning algorithms ]] were used on the same << classification task >> using the Principal Components of the texture and flowfield representation of the faces . | USED-FOR | [
1,
3,
9,
10
] |
Several << hyperplane learning algorithms >> were used on the same classification task using the [[ Principal Components of the texture ]] and flowfield representation of the faces . | USED-FOR | [
13,
17,
1,
3
] |
Several << hyperplane learning algorithms >> were used on the same classification task using the Principal Components of the texture and [[ flowfield representation of the faces ]] . | USED-FOR | [
19,
23,
1,
3
] |
Several hyperplane learning algorithms were used on the same classification task using the << Principal Components of the texture >> and [[ flowfield representation of the faces ]] . | CONJUNCTION | [
19,
23,
13,
17
] |
The << classification >> performance of the [[ learning algorithms ]] was estimated using the face database with the true gender of the faces as labels , and also with the gender estimated by the subjects . | USED-FOR | [
5,
6,
1,
1
] |
The classification performance of the << learning algorithms >> was estimated using the [[ face database ]] with the true gender of the faces as labels , and also with the gender estimated by the subjects . | EVALUATE-FOR | [
11,
12,
5,
6
] |
Our results suggest that << human classification >> can be modeled by some [[ hyperplane algorithms ]] in the feature space we used . | USED-FOR | [
11,
12,
4,
5
] |
Our results suggest that human classification can be modeled by some << hyperplane algorithms >> in the [[ feature space ]] we used . | FEATURE-OF | [
15,
16,
11,
12
] |
For classification , the brain needs more processing for stimuli close to that [[ hyperplane ]] than for << those >> further away . | COMPARE | [
13,
13,
16,
16
] |
In this paper , we present a [[ corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system ]] for << Dutch >> which combines statistical classification -LRB- maximum entropy -RRB- with linguistic information . | USED-FOR | [
7,
15,
17,
17
] |
In this paper , we present a << corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system >> for Dutch which combines [[ statistical classification ]] -LRB- maximum entropy -RRB- with linguistic information . | PART-OF | [
20,
21,
7,
15
] |
In this paper , we present a << corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system >> for Dutch which combines statistical classification -LRB- [[ maximum entropy ]] -RRB- with linguistic information . | PART-OF | [
23,
24,
7,
15
] |
In this paper , we present a << corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system >> for Dutch which combines statistical classification -LRB- maximum entropy -RRB- with [[ linguistic information ]] . | PART-OF | [
27,
28,
7,
15
] |
In this paper , we present a corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system for Dutch which combines statistical classification -LRB- << maximum entropy >> -RRB- with [[ linguistic information ]] . | CONJUNCTION | [
27,
28,
23,
24
] |
Instead of building individual [[ classifiers ]] per ambiguous wordform , we introduce a << lemma-based approach >> . | COMPARE | [
4,
4,
12,
13
] |
Instead of building individual << classifiers >> per [[ ambiguous wordform ]] , we introduce a lemma-based approach . | USED-FOR | [
6,
7,
4,
4
] |
The advantage of this novel method is that it clusters all [[ inflected forms ]] of an << ambiguous word >> in one classifier , therefore augmenting the training material available to the algorithm . | FEATURE-OF | [
11,
12,
15,
16
] |
Testing the [[ lemma-based model ]] on the Dutch Senseval-2 test data , we achieve a significant increase in accuracy over the << wordform model >> . | COMPARE | [
2,
3,
20,
21
] |
Testing the << lemma-based model >> on the [[ Dutch Senseval-2 test data ]] , we achieve a significant increase in accuracy over the wordform model . | EVALUATE-FOR | [
6,
9,
2,
3
] |
We propose an exact , general and efficient [[ coarse-to-fine energy minimization strategy ]] for << semantic video segmenta-tion >> . | USED-FOR | [
8,
11,
13,
15
] |
Our << strategy >> is based on a [[ hierarchical abstraction of the supervoxel graph ]] that allows us to minimize an energy defined at the finest level of the hierarchy by minimizing a series of simpler energies defined over coarser graphs . | USED-FOR | [
6,
11,
1,
1
] |
It is general , i.e. , [[ it ]] can be used to minimize any << energy function >> -LRB- e.g. , unary , pairwise , and higher-order terms -RRB- with any existing energy minimization algorithm -LRB- e.g. , graph cuts and belief propagation -RRB- . | USED-FOR | [
6,
6,
13,
14
] |
It is general , i.e. , [[ it ]] can be used to minimize any energy function -LRB- e.g. , unary , pairwise , and higher-order terms -RRB- with any existing << energy minimization algorithm >> -LRB- e.g. , graph cuts and belief propagation -RRB- . | CONJUNCTION | [
6,
6,
29,
31
] |
It is general , i.e. , it can be used to minimize any << energy function >> -LRB- e.g. , unary , pairwise , and higher-order terms -RRB- with any existing [[ energy minimization algorithm ]] -LRB- e.g. , graph cuts and belief propagation -RRB- . | USED-FOR | [
29,
31,
13,
14
] |
It is general , i.e. , it can be used to minimize any energy function -LRB- e.g. , unary , pairwise , and higher-order terms -RRB- with any existing << energy minimization algorithm >> -LRB- e.g. , [[ graph cuts ]] and belief propagation -RRB- . | HYPONYM-OF | [
35,
36,
29,
31
] |
It is general , i.e. , it can be used to minimize any energy function -LRB- e.g. , unary , pairwise , and higher-order terms -RRB- with any existing energy minimization algorithm -LRB- e.g. , [[ graph cuts ]] and << belief propagation >> -RRB- . | CONJUNCTION | [
35,
36,
38,
39
] |
It is general , i.e. , it can be used to minimize any energy function -LRB- e.g. , unary , pairwise , and higher-order terms -RRB- with any existing << energy minimization algorithm >> -LRB- e.g. , graph cuts and [[ belief propagation ]] -RRB- . | HYPONYM-OF | [
38,
39,
29,
31
] |
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