text stringlengths 49 577 | label stringclasses 7
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|---|---|---|
This paper proposes an [[ approach ]] to << full parsing >> suitable for Information Extraction from texts . | USED-FOR | [
4,
4,
6,
7
] |
This paper proposes an approach to [[ full parsing ]] suitable for << Information Extraction >> from texts . | USED-FOR | [
6,
7,
10,
11
] |
[[ It ]] was implemented in the << IE module >> of FACILE , a EU project for multilingual text classification and IE . | USED-FOR | [
0,
0,
5,
6
] |
It was implemented in the [[ IE module ]] of << FACILE , a EU project for multilingual text classification and IE >> . | PART-OF | [
5,
6,
8,
18
] |
It then presents an implemented << graphic interpretation system >> that takes into account a variety of [[ communicative signals ]] , and an evaluation study showing that evidence obtained from shallow processing of the graphic 's caption has a significant impact on the system 's success . | USED-FOR | [
15,
16,
5,
7
] |
It then presents an implemented graphic interpretation system that takes into account a variety of communicative signals , and an evaluation study showing that evidence obtained from [[ shallow processing ]] of the graphic 's caption has a significant impact on the << system >> 's success . | USED-FOR | [
27,
28,
40,
40
] |
It then presents an implemented graphic interpretation system that takes into account a variety of communicative signals , and an evaluation study showing that evidence obtained from << shallow processing >> of the [[ graphic 's caption ]] has a significant impact on the system 's success . | USED-FOR | [
31,
33,
27,
28
] |
[[ Graphical models ]] such as Bayesian Networks -LRB- BNs -RRB- are being increasingly applied to various << computer vision problems >> . | USED-FOR | [
0,
1,
15,
17
] |
<< Graphical models >> such as [[ Bayesian Networks -LRB- BNs -RRB- ]] are being increasingly applied to various computer vision problems . | HYPONYM-OF | [
4,
8,
0,
1
] |
One bottleneck in using BN is that learning the << BN model parameters >> often requires a large amount of reliable and [[ representative training data ]] , which proves to be difficult to acquire for many computer vision tasks . | USED-FOR | [
20,
22,
9,
11
] |
One bottleneck in using BN is that learning the BN model parameters often requires a large amount of reliable and [[ representative training data ]] , which proves to be difficult to acquire for many << computer vision tasks >> . | USED-FOR | [
20,
22,
33,
35
] |
On the other hand , there is often available [[ qualitative prior knowledge ]] about the << model >> . | FEATURE-OF | [
9,
11,
14,
14
] |
Such << knowledge >> comes either from [[ domain experts ]] based on their experience or from various physical or geometric constraints that govern the objects we try to model . | USED-FOR | [
5,
6,
1,
1
] |
Such knowledge comes either from [[ domain experts ]] based on their experience or from various << physical or geometric constraints >> that govern the objects we try to model . | CONJUNCTION | [
5,
6,
14,
17
] |
Such << knowledge >> comes either from domain experts based on their experience or from various [[ physical or geometric constraints ]] that govern the objects we try to model . | USED-FOR | [
14,
17,
1,
1
] |
Unlike the [[ quantitative prior ]] , the << qualitative prior >> is often ignored due to the difficulty of incorporating them into the model learning process . | COMPARE | [
2,
3,
6,
7
] |
Unlike the quantitative prior , the qualitative prior is often ignored due to the difficulty of incorporating [[ them ]] into the << model learning process >> . | PART-OF | [
17,
17,
20,
22
] |
In this paper , we introduce a closed-form solution to systematically combine the [[ limited training data ]] with some generic << qualitative knowledge >> for BN parameter learning . | CONJUNCTION | [
13,
15,
19,
20
] |
In this paper , we introduce a closed-form solution to systematically combine the [[ limited training data ]] with some generic qualitative knowledge for << BN parameter learning >> . | USED-FOR | [
13,
15,
22,
24
] |
In this paper , we introduce a closed-form solution to systematically combine the limited training data with some generic [[ qualitative knowledge ]] for << BN parameter learning >> . | USED-FOR | [
19,
20,
22,
24
] |
In this paper , we introduce a << closed-form solution >> to systematically combine the limited training data with some generic qualitative knowledge for [[ BN parameter learning ]] . | USED-FOR | [
22,
24,
7,
8
] |
To validate our method , we compare [[ it ]] with the << Maximum Likelihood -LRB- ML -RRB- estimation method >> under sparse data and with the Expectation Maximization -LRB- EM -RRB- algorithm under incomplete data respectively . | COMPARE | [
7,
7,
10,
16
] |
To validate our method , we compare [[ it ]] with the Maximum Likelihood -LRB- ML -RRB- estimation method under sparse data and with the << Expectation Maximization -LRB- EM -RRB- algorithm >> under incomplete data respectively . | COMPARE | [
7,
7,
23,
28
] |
To validate our method , we compare << it >> with the Maximum Likelihood -LRB- ML -RRB- estimation method under [[ sparse data ]] and with the Expectation Maximization -LRB- EM -RRB- algorithm under incomplete data respectively . | USED-FOR | [
18,
19,
7,
7
] |
To validate our method , we compare it with the << Maximum Likelihood -LRB- ML -RRB- estimation method >> under [[ sparse data ]] and with the Expectation Maximization -LRB- EM -RRB- algorithm under incomplete data respectively . | USED-FOR | [
18,
19,
10,
16
] |
To validate our method , we compare << it >> with the Maximum Likelihood -LRB- ML -RRB- estimation method under sparse data and with the Expectation Maximization -LRB- EM -RRB- algorithm under [[ incomplete data ]] respectively . | USED-FOR | [
30,
31,
7,
7
] |
To validate our method , we compare it with the Maximum Likelihood -LRB- ML -RRB- estimation method under sparse data and with the << Expectation Maximization -LRB- EM -RRB- algorithm >> under [[ incomplete data ]] respectively . | USED-FOR | [
30,
31,
23,
28
] |
To further demonstrate its applications for << computer vision >> , we apply [[ it ]] to learn a BN model for facial Action Unit -LRB- AU -RRB- recognition from real image data . | USED-FOR | [
11,
11,
6,
7
] |
To further demonstrate its applications for computer vision , we apply [[ it ]] to learn a << BN model >> for facial Action Unit -LRB- AU -RRB- recognition from real image data . | USED-FOR | [
11,
11,
15,
16
] |
To further demonstrate its applications for computer vision , we apply it to learn a [[ BN model ]] for << facial Action Unit -LRB- AU -RRB- recognition >> from real image data . | USED-FOR | [
15,
16,
18,
24
] |
To further demonstrate its applications for computer vision , we apply it to learn a BN model for << facial Action Unit -LRB- AU -RRB- recognition >> from [[ real image data ]] . | USED-FOR | [
26,
28,
18,
24
] |
The experimental results show that with simple and [[ generic qualitative constraints ]] and using only a small amount of << training data >> , our method can robustly and accurately estimate the BN model parameters . | CONJUNCTION | [
8,
10,
18,
19
] |
The experimental results show that with simple and [[ generic qualitative constraints ]] and using only a small amount of training data , our << method >> can robustly and accurately estimate the BN model parameters . | USED-FOR | [
8,
10,
22,
22
] |
The experimental results show that with simple and generic qualitative constraints and using only a small amount of [[ training data ]] , our << method >> can robustly and accurately estimate the BN model parameters . | USED-FOR | [
18,
19,
22,
22
] |
The experimental results show that with simple and generic qualitative constraints and using only a small amount of training data , our [[ method ]] can robustly and accurately estimate the << BN model parameters >> . | USED-FOR | [
22,
22,
29,
31
] |
In this paper we introduce a [[ modal language LT ]] for imposing << constraints on trees >> , and an extension LT -LRB- LF -RRB- for imposing constraints on trees decorated with feature structures . | USED-FOR | [
6,
8,
11,
13
] |
In this paper we introduce a modal language LT for imposing constraints on trees , and an [[ extension LT -LRB- LF -RRB- ]] for imposing << constraints on trees decorated with feature structures >> . | USED-FOR | [
17,
21,
24,
30
] |
The motivation for introducing these [[ languages ]] is to provide tools for formalising << grammatical frameworks >> perspicuously , and the paper illustrates this by showing how the leading ideas of GPSG can be captured in LT -LRB- LF -RRB- . | USED-FOR | [
5,
5,
12,
13
] |
The motivation for introducing these languages is to provide tools for formalising grammatical frameworks perspicuously , and the paper illustrates this by showing how the leading ideas of [[ GPSG ]] can be captured in << LT -LRB- LF -RRB- >> . | USED-FOR | [
28,
28,
33,
36
] |
Previous research has demonstrated the utility of [[ clustering ]] in << inducing semantic verb classes >> from undisambiguated corpus data . | USED-FOR | [
7,
7,
9,
12
] |
Previous research has demonstrated the utility of << clustering >> in inducing semantic verb classes from [[ undisambiguated corpus data ]] . | USED-FOR | [
14,
16,
7,
7
] |
We describe a new << approach >> which involves [[ clustering subcategorization frame -LRB- SCF -RRB- distributions ]] using the Information Bottleneck and nearest neighbour methods . | PART-OF | [
7,
13,
4,
4
] |
We describe a new approach which involves << clustering subcategorization frame -LRB- SCF -RRB- distributions >> using the [[ Information Bottleneck and nearest neighbour methods ]] . | USED-FOR | [
16,
21,
7,
13
] |
A novel [[ evaluation scheme ]] is proposed which accounts for the effect of << polysemy >> on the clusters , offering us a good insight into the potential and limitations of semantically classifying undisambiguated SCF data . | USED-FOR | [
2,
3,
12,
12
] |
A novel [[ evaluation scheme ]] is proposed which accounts for the effect of polysemy on the clusters , offering us a good insight into the potential and limitations of << semantically classifying undisambiguated SCF data >> . | EVALUATE-FOR | [
2,
3,
28,
32
] |
A novel evaluation scheme is proposed which accounts for the effect of [[ polysemy ]] on the << clusters >> , offering us a good insight into the potential and limitations of semantically classifying undisambiguated SCF data . | FEATURE-OF | [
12,
12,
15,
15
] |
Due to the capacity of [[ pan-tilt-zoom -LRB- PTZ -RRB- cameras ]] to simultaneously cover a << panoramic area >> and maintain high resolution imagery , researches in automated surveillance systems with multiple PTZ cameras have become increasingly important . | USED-FOR | [
5,
9,
14,
15
] |
Due to the capacity of [[ pan-tilt-zoom -LRB- PTZ -RRB- cameras ]] to simultaneously cover a panoramic area and maintain << high resolution imagery >> , researches in automated surveillance systems with multiple PTZ cameras have become increasingly important . | USED-FOR | [
5,
9,
18,
20
] |
Due to the capacity of pan-tilt-zoom -LRB- PTZ -RRB- cameras to simultaneously cover a panoramic area and maintain high resolution imagery , researches in << automated surveillance systems >> with multiple [[ PTZ cameras ]] have become increasingly important . | FEATURE-OF | [
29,
30,
24,
26
] |
Most existing [[ algorithms ]] require the prior knowledge of intrinsic parameters of the PTZ camera to infer the << relative positioning >> and orientation among multiple PTZ cameras . | USED-FOR | [
2,
2,
17,
18
] |
Most existing [[ algorithms ]] require the prior knowledge of intrinsic parameters of the PTZ camera to infer the relative positioning and << orientation >> among multiple PTZ cameras . | USED-FOR | [
2,
2,
20,
20
] |
Most existing << algorithms >> require the [[ prior knowledge of intrinsic parameters of the PTZ camera ]] to infer the relative positioning and orientation among multiple PTZ cameras . | USED-FOR | [
5,
13,
2,
2
] |
Most existing algorithms require the prior knowledge of intrinsic parameters of the PTZ camera to infer the [[ relative positioning ]] and << orientation >> among multiple PTZ cameras . | CONJUNCTION | [
17,
18,
20,
20
] |
Most existing algorithms require the prior knowledge of intrinsic parameters of the PTZ camera to infer the [[ relative positioning ]] and orientation among multiple << PTZ cameras >> . | FEATURE-OF | [
17,
18,
23,
24
] |
Most existing algorithms require the prior knowledge of intrinsic parameters of the PTZ camera to infer the relative positioning and [[ orientation ]] among multiple << PTZ cameras >> . | FEATURE-OF | [
20,
20,
23,
24
] |
To overcome this limitation , we propose a novel [[ mapping algorithm ]] that derives the << relative positioning >> and orientation between two PTZ cameras based on a unified polynomial model . | USED-FOR | [
9,
10,
14,
15
] |
To overcome this limitation , we propose a novel [[ mapping algorithm ]] that derives the relative positioning and << orientation >> between two PTZ cameras based on a unified polynomial model . | USED-FOR | [
9,
10,
17,
17
] |
To overcome this limitation , we propose a novel mapping algorithm that derives the [[ relative positioning ]] and << orientation >> between two PTZ cameras based on a unified polynomial model . | CONJUNCTION | [
14,
15,
17,
17
] |
To overcome this limitation , we propose a novel mapping algorithm that derives the [[ relative positioning ]] and orientation between two << PTZ cameras >> based on a unified polynomial model . | FEATURE-OF | [
14,
15,
20,
21
] |
To overcome this limitation , we propose a novel mapping algorithm that derives the relative positioning and [[ orientation ]] between two << PTZ cameras >> based on a unified polynomial model . | FEATURE-OF | [
17,
17,
20,
21
] |
To overcome this limitation , we propose a novel << mapping algorithm >> that derives the relative positioning and orientation between two PTZ cameras based on a [[ unified polynomial model ]] . | USED-FOR | [
25,
27,
9,
10
] |
Experimental results demonstrate that our proposed << algorithm >> presents substantially reduced [[ computational complexity ]] and improved flexibility at the cost of slightly decreased pixel accuracy , as compared with the work of Chen and Wang . | EVALUATE-FOR | [
10,
11,
6,
6
] |
Experimental results demonstrate that our proposed << algorithm >> presents substantially reduced computational complexity and improved [[ flexibility ]] at the cost of slightly decreased pixel accuracy , as compared with the work of Chen and Wang . | EVALUATE-FOR | [
14,
14,
6,
6
] |
Experimental results demonstrate that our proposed << algorithm >> presents substantially reduced computational complexity and improved flexibility at the cost of slightly decreased [[ pixel accuracy ]] , as compared with the work of Chen and Wang . | EVALUATE-FOR | [
21,
22,
6,
6
] |
This slightly decreased << pixel accuracy >> can be compensated by [[ consistent labeling approaches ]] without added cost for the application of automated surveillance systems along with changing configurations and a larger number of PTZ cameras . | USED-FOR | [
9,
11,
3,
4
] |
This paper presents a new [[ two-pass algorithm ]] for << Extra Large -LRB- more than 1M words -RRB- Vocabulary COntinuous Speech recognition >> based on the Information Retrieval -LRB- ELVIRCOS -RRB- . | USED-FOR | [
5,
6,
8,
19
] |
This paper presents a new << two-pass algorithm >> for Extra Large -LRB- more than 1M words -RRB- Vocabulary COntinuous Speech recognition based on the [[ Information Retrieval -LRB- ELVIRCOS -RRB- ]] . | USED-FOR | [
23,
27,
5,
6
] |
The principle of this approach is to decompose a recognition process into two << passes >> where the [[ first pass ]] builds the words subset for the second pass recognition by using information retrieval procedure . | HYPONYM-OF | [
16,
17,
13,
13
] |
The principle of this approach is to decompose a recognition process into two << passes >> where the first pass builds the words subset for the [[ second pass recognition ]] by using information retrieval procedure . | HYPONYM-OF | [
24,
26,
13,
13
] |
The principle of this approach is to decompose a recognition process into two passes where the first pass builds the words subset for the << second pass recognition >> by using [[ information retrieval procedure ]] . | USED-FOR | [
29,
31,
24,
26
] |
[[ Word graph composition ]] for << continuous speech >> is presented . | USED-FOR | [
0,
2,
4,
5
] |
With this [[ approach ]] a high performances for << large vocabulary speech recognition >> can be obtained . | USED-FOR | [
2,
2,
7,
10
] |
First , images are partitioned into regions using << one-class classification >> and [[ patch-based clustering algorithms ]] where one-class classifiers model the regions with relatively uniform color and texture properties , and clustering of patches aims to detect structures in the remaining regions . | CONJUNCTION | [
11,
13,
8,
9
] |
First , images are partitioned into regions using one-class classification and patch-based clustering algorithms where << one-class classifiers >> model the regions with relatively [[ uniform color and texture properties ]] , and clustering of patches aims to detect structures in the remaining regions . | USED-FOR | [
22,
26,
15,
16
] |
Next , the resulting regions are clustered to obtain a codebook of region types , and two [[ models ]] are constructed for << scene representation >> : a '' bag of individual regions '' representation where each region is regarded separately , and a '' bag of region pairs '' representation where regions with particular... | USED-FOR | [
17,
17,
21,
22
] |
Given these representations , << scene classification >> is done using [[ Bayesian classifiers ]] . | USED-FOR | [
9,
10,
4,
5
] |
Experiments on the [[ LabelMe data set ]] showed that the proposed << models >> significantly out-perform a baseline global feature-based approach . | EVALUATE-FOR | [
3,
5,
10,
10
] |
Experiments on the [[ LabelMe data set ]] showed that the proposed models significantly out-perform a << baseline global feature-based approach >> . | EVALUATE-FOR | [
3,
5,
14,
17
] |
Experiments on the LabelMe data set showed that the proposed [[ models ]] significantly out-perform a << baseline global feature-based approach >> . | COMPARE | [
10,
10,
14,
17
] |
The [[ model ]] is designed for use in << error correction >> , with a focus on post-processing the output of black-box OCR systems in order to make it more useful for NLP tasks . | USED-FOR | [
1,
1,
7,
8
] |
The [[ model ]] is designed for use in error correction , with a focus on << post-processing >> the output of black-box OCR systems in order to make it more useful for NLP tasks . | USED-FOR | [
1,
1,
14,
14
] |
The model is designed for use in << error correction >> , with a focus on [[ post-processing ]] the output of black-box OCR systems in order to make it more useful for NLP tasks . | PART-OF | [
14,
14,
7,
8
] |
The model is designed for use in error correction , with a focus on post-processing the output of black-box OCR systems in order to make [[ it ]] more useful for << NLP tasks >> . | USED-FOR | [
25,
25,
29,
30
] |
We present an implementation of the << model >> based on [[ finite-state models ]] , demonstrate the model 's ability to significantly reduce character and word error rate , and provide evaluation results involving automatic extraction of translation lexicons from printed text . | USED-FOR | [
9,
10,
6,
6
] |
We present an implementation of the model based on finite-state models , demonstrate the << model >> 's ability to significantly reduce [[ character and word error rate ]] , and provide evaluation results involving automatic extraction of translation lexicons from printed text . | EVALUATE-FOR | [
20,
24,
14,
14
] |
We present an implementation of the model based on finite-state models , demonstrate the << model >> 's ability to significantly reduce character and word error rate , and provide evaluation results involving [[ automatic extraction of translation lexicons ]] from printed text . | EVALUATE-FOR | [
31,
35,
14,
14
] |
We present an implementation of the model based on finite-state models , demonstrate the model 's ability to significantly reduce character and word error rate , and provide evaluation results involving << automatic extraction of translation lexicons >> from [[ printed text ]] . | USED-FOR | [
37,
38,
31,
35
] |
We present a [[ framework ]] for << word alignment >> based on log-linear models . | USED-FOR | [
3,
3,
5,
6
] |
We present a << framework >> for word alignment based on [[ log-linear models ]] . | USED-FOR | [
9,
10,
3,
3
] |
All [[ knowledge sources ]] are treated as << feature functions >> , which depend on the source langauge sentence , the target language sentence and possible additional variables . | USED-FOR | [
1,
2,
6,
7
] |
[[ Log-linear models ]] allow << statistical alignment models >> to be easily extended by incorporating syntactic information . | USED-FOR | [
0,
1,
3,
5
] |
<< Log-linear models >> allow statistical alignment models to be easily extended by incorporating [[ syntactic information ]] . | USED-FOR | [
12,
13,
0,
1
] |
In this paper , we use [[ IBM Model 3 alignment probabilities ]] , << POS correspondence >> , and bilingual dictionary coverage as features . | CONJUNCTION | [
6,
10,
12,
13
] |
In this paper , we use [[ IBM Model 3 alignment probabilities ]] , POS correspondence , and bilingual dictionary coverage as << features >> . | USED-FOR | [
6,
10,
20,
20
] |
In this paper , we use IBM Model 3 alignment probabilities , [[ POS correspondence ]] , and << bilingual dictionary coverage >> as features . | CONJUNCTION | [
12,
13,
16,
18
] |
In this paper , we use IBM Model 3 alignment probabilities , [[ POS correspondence ]] , and bilingual dictionary coverage as << features >> . | USED-FOR | [
12,
13,
20,
20
] |
In this paper , we use IBM Model 3 alignment probabilities , POS correspondence , and [[ bilingual dictionary coverage ]] as << features >> . | USED-FOR | [
16,
18,
20,
20
] |
Our experiments show that [[ log-linear models ]] significantly outperform << IBM translation models >> . | COMPARE | [
4,
5,
8,
10
] |
[[ Hough voting ]] in a geometric transformation space allows us to realize << spatial verification >> , but remains sensitive to feature detection errors because of the inflexible quan-tization of single feature correspondences . | USED-FOR | [
0,
1,
11,
12
] |
<< Hough voting >> in a [[ geometric transformation space ]] allows us to realize spatial verification , but remains sensitive to feature detection errors because of the inflexible quan-tization of single feature correspondences . | FEATURE-OF | [
4,
6,
0,
1
] |
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