text stringlengths 49 577 | label stringclasses 7
values | metadata list |
|---|---|---|
With a [[ sentence-aligned corpus ]] , << translation equivalences >> are suggested by analysing the frequency profiles of parallel concordances . | USED-FOR | [
2,
3,
5,
6
] |
With a sentence-aligned corpus , translation equivalences are suggested by analysing the [[ frequency profiles ]] of << parallel concordances >> . | PART-OF | [
12,
13,
15,
16
] |
The [[ method ]] overcomes the limitations of conventional << statistical methods >> which require large corpora to be effective , and lexical approaches which depend on existing bilingual dictionaries . | COMPARE | [
1,
1,
7,
8
] |
The [[ method ]] overcomes the limitations of conventional statistical methods which require large corpora to be effective , and << lexical approaches >> which depend on existing bilingual dictionaries . | COMPARE | [
1,
1,
18,
19
] |
The method overcomes the limitations of conventional << statistical methods >> which require [[ large corpora ]] to be effective , and lexical approaches which depend on existing bilingual dictionaries . | USED-FOR | [
11,
12,
7,
8
] |
The method overcomes the limitations of conventional statistical methods which require large corpora to be effective , and << lexical approaches >> which depend on existing [[ bilingual dictionaries ]] . | USED-FOR | [
24,
25,
18,
19
] |
Pilot testing on a parallel corpus of about 113K Chinese words and 120K English words gives an encouraging 85 % [[ precision ]] and 45 % << recall >> . | CONJUNCTION | [
20,
20,
24,
24
] |
Future work includes fine-tuning the algorithm upon the analysis of the errors , and acquiring a [[ translation lexicon ]] for << legal terminology >> by filtering out general terms . | USED-FOR | [
16,
17,
19,
20
] |
Traditional [[ machine learning techniques ]] have been applied to this << problem >> with reasonable success , but they have been shown to work well only when there is a good match between the training and test data with respect to topic . | USED-FOR | [
1,
3,
9,
9
] |
This paper demonstrates that match with respect to domain and time is also important , and presents preliminary experiments with << training data >> labeled with [[ emoticons ]] , which has the potential of being independent of domain , topic and time . | FEATURE-OF | [
24,
24,
20,
21
] |
We present a novel [[ algorithm ]] for estimating the broad << 3D geometric structure of outdoor video scenes >> . | USED-FOR | [
4,
4,
9,
15
] |
Leveraging [[ spatio-temporal video segmentation ]] , we decompose a << dynamic scene >> captured by a video into geometric classes , based on predictions made by region-classifiers that are trained on appearance and motion features . | USED-FOR | [
1,
3,
8,
9
] |
Leveraging spatio-temporal video segmentation , we decompose a << dynamic scene >> captured by a video into [[ geometric classes ]] , based on predictions made by region-classifiers that are trained on appearance and motion features . | PART-OF | [
15,
16,
8,
9
] |
Leveraging spatio-temporal video segmentation , we decompose a dynamic scene captured by a video into << geometric classes >> , based on predictions made by [[ region-classifiers ]] that are trained on appearance and motion features . | USED-FOR | [
23,
23,
15,
16
] |
Leveraging spatio-temporal video segmentation , we decompose a dynamic scene captured by a video into geometric classes , based on predictions made by << region-classifiers >> that are trained on [[ appearance and motion features ]] . | USED-FOR | [
28,
31,
23,
23
] |
We built a novel , extensive [[ dataset ]] on geometric context of video to evaluate our << method >> , consisting of over 100 ground-truth annotated outdoor videos with over 20,000 frames . | EVALUATE-FOR | [
6,
6,
15,
15
] |
We built a novel , extensive << dataset >> on [[ geometric context of video ]] to evaluate our method , consisting of over 100 ground-truth annotated outdoor videos with over 20,000 frames . | FEATURE-OF | [
8,
11,
6,
6
] |
We built a novel , extensive << dataset >> on geometric context of video to evaluate our method , consisting of over 100 ground-truth [[ annotated outdoor videos ]] with over 20,000 frames . | PART-OF | [
22,
24,
6,
6
] |
To further scale beyond this dataset , we propose a [[ semi-supervised learning framework ]] to expand the pool of << labeled data >> with high confidence predictions obtained from unlabeled data . | USED-FOR | [
10,
12,
18,
19
] |
To further scale beyond this dataset , we propose a << semi-supervised learning framework >> to expand the pool of labeled data with [[ high confidence predictions ]] obtained from unlabeled data . | USED-FOR | [
21,
23,
10,
12
] |
To further scale beyond this dataset , we propose a semi-supervised learning framework to expand the pool of labeled data with << high confidence predictions >> obtained from [[ unlabeled data ]] . | USED-FOR | [
26,
27,
21,
23
] |
Our [[ system ]] produces an accurate prediction of << geometric context of video >> achieving 96 % accuracy across main geometric classes . | USED-FOR | [
1,
1,
7,
10
] |
Our << system >> produces an accurate prediction of geometric context of video achieving 96 % [[ accuracy ]] across main geometric classes . | EVALUATE-FOR | [
14,
14,
1,
1
] |
This paper describes a [[ system ]] -LRB- RAREAS -RRB- which synthesizes << marine weather forecasts >> directly from formatted weather data . | USED-FOR | [
4,
4,
10,
12
] |
This paper describes a << system >> -LRB- RAREAS -RRB- which synthesizes marine weather forecasts directly from [[ formatted weather data ]] . | USED-FOR | [
15,
17,
4,
4
] |
Such << synthesis >> appears feasible in certain [[ natural sublanguages with stereotyped text structure ]] . | USED-FOR | [
6,
11,
1,
1
] |
<< RAREAS >> draws on several kinds of [[ linguistic and non-linguistic knowledge ]] and mirrors a forecaster 's apparent tendency to ascribe less precise temporal adverbs to more remote meteorological events . | USED-FOR | [
6,
9,
0,
0
] |
The << approach >> can easily be adapted to synthesize [[ bilingual or multi-lingual texts ]] . | USED-FOR | [
8,
11,
1,
1
] |
We go , on to describe [[ FlexP ]] , a << bottom-up pattern-matching parser >> that we have designed and implemented to provide these flexibilities for restricted natural language input to a limited-domain computer system . | HYPONYM-OF | [
6,
6,
9,
11
] |
We go , on to describe FlexP , a [[ bottom-up pattern-matching parser ]] that we have designed and implemented to provide these << flexibilities >> for restricted natural language input to a limited-domain computer system . | USED-FOR | [
9,
11,
21,
21
] |
We go , on to describe FlexP , a bottom-up pattern-matching parser that we have designed and implemented to provide these [[ flexibilities ]] for << restricted natural language >> input to a limited-domain computer system . | FEATURE-OF | [
21,
21,
23,
25
] |
We go , on to describe FlexP , a bottom-up pattern-matching parser that we have designed and implemented to provide these [[ flexibilities ]] for restricted natural language input to a << limited-domain computer system >> . | PART-OF | [
21,
21,
29,
31
] |
We go , on to describe FlexP , a << bottom-up pattern-matching parser >> that we have designed and implemented to provide these flexibilities for [[ restricted natural language ]] input to a limited-domain computer system . | USED-FOR | [
23,
25,
9,
11
] |
Traditional << information retrieval techniques >> use a [[ histogram of keywords ]] as the document representation but oral communication may offer additional indices such as the time and place of the rejoinder and the attendance . | USED-FOR | [
6,
8,
1,
3
] |
Traditional information retrieval techniques use a [[ histogram of keywords ]] as the << document representation >> but oral communication may offer additional indices such as the time and place of the rejoinder and the attendance . | USED-FOR | [
6,
8,
11,
12
] |
An alternative index could be the << activity >> such as [[ discussing ]] , planning , informing , story-telling , etc. . | HYPONYM-OF | [
9,
9,
6,
6
] |
An alternative index could be the activity such as [[ discussing ]] , << planning >> , informing , story-telling , etc. . | CONJUNCTION | [
9,
9,
11,
11
] |
An alternative index could be the << activity >> such as discussing , [[ planning ]] , informing , story-telling , etc. . | HYPONYM-OF | [
11,
11,
6,
6
] |
An alternative index could be the activity such as discussing , [[ planning ]] , << informing >> , story-telling , etc. . | CONJUNCTION | [
11,
11,
13,
13
] |
An alternative index could be the << activity >> such as discussing , planning , [[ informing ]] , story-telling , etc. . | HYPONYM-OF | [
13,
13,
6,
6
] |
An alternative index could be the activity such as discussing , planning , [[ informing ]] , << story-telling >> , etc. . | CONJUNCTION | [
13,
13,
15,
15
] |
An alternative index could be the << activity >> such as discussing , planning , informing , [[ story-telling ]] , etc. . | HYPONYM-OF | [
15,
15,
6,
6
] |
This paper addresses the problem of the << automatic detection >> of those [[ activities ]] in meeting situation and everyday rejoinders . | USED-FOR | [
11,
11,
7,
8
] |
The format of the << corpus >> adopts the [[ Child Language Data Exchange System -LRB- CHILDES -RRB- ]] . | FEATURE-OF | [
7,
14,
4,
4
] |
In this paper , we describe [[ data collection ]] , << transcription >> , word segmentation , and part-of-speech annotation of this corpus . | CONJUNCTION | [
6,
7,
9,
9
] |
In this paper , we describe [[ data collection ]] , transcription , word segmentation , and part-of-speech annotation of this << corpus >> . | USED-FOR | [
6,
7,
19,
19
] |
In this paper , we describe data collection , [[ transcription ]] , << word segmentation >> , and part-of-speech annotation of this corpus . | CONJUNCTION | [
9,
9,
11,
12
] |
In this paper , we describe data collection , [[ transcription ]] , word segmentation , and part-of-speech annotation of this << corpus >> . | USED-FOR | [
9,
9,
19,
19
] |
In this paper , we describe data collection , transcription , [[ word segmentation ]] , and << part-of-speech annotation >> of this corpus . | CONJUNCTION | [
11,
12,
15,
16
] |
In this paper , we describe data collection , transcription , [[ word segmentation ]] , and part-of-speech annotation of this << corpus >> . | USED-FOR | [
11,
12,
19,
19
] |
In this paper , we describe data collection , transcription , word segmentation , and [[ part-of-speech annotation ]] of this << corpus >> . | USED-FOR | [
15,
16,
19,
19
] |
This paper shows how << dictionary word sense definitions >> can be analysed by applying a [[ hierarchy of phrasal patterns ]] . | USED-FOR | [
14,
17,
4,
7
] |
An experimental << system >> embodying this [[ mechanism ]] has been implemented for processing definitions from the Longman Dictionary of Contemporary English . | PART-OF | [
5,
5,
2,
2
] |
A property of this dictionary , exploited by the system , is that << it >> uses a [[ restricted vocabulary ]] in its word sense definitions . | USED-FOR | [
16,
17,
13,
13
] |
A property of this dictionary , exploited by the system , is that it uses a [[ restricted vocabulary ]] in its << word sense definitions >> . | USED-FOR | [
16,
17,
20,
22
] |
The structures generated by the experimental system are intended to be used for the << classification of new word senses >> in terms of the senses of words in the [[ restricted vocabulary ]] . | USED-FOR | [
28,
29,
14,
18
] |
Thus the work reported addresses two [[ robustness problems ]] faced by current experimental << natural language processing systems >> : coping with an incomplete lexicon and with incomplete knowledge of phrasal constructions . | FEATURE-OF | [
6,
7,
12,
15
] |
Thus the work reported addresses two << robustness problems >> faced by current experimental natural language processing systems : coping with an [[ incomplete lexicon ]] and with incomplete knowledge of phrasal constructions . | HYPONYM-OF | [
20,
21,
6,
7
] |
Thus the work reported addresses two << robustness problems >> faced by current experimental natural language processing systems : coping with an incomplete lexicon and with [[ incomplete knowledge of phrasal constructions ]] . | HYPONYM-OF | [
24,
28,
6,
7
] |
This paper presents a << word segmentation system >> in France Telecom R&D Beijing , which uses a unified [[ approach ]] to word breaking and OOV identification . | USED-FOR | [
17,
17,
4,
6
] |
This paper presents a word segmentation system in France Telecom R&D Beijing , which uses a unified [[ approach ]] to << word breaking >> and OOV identification . | USED-FOR | [
17,
17,
19,
20
] |
This paper presents a word segmentation system in France Telecom R&D Beijing , which uses a unified [[ approach ]] to word breaking and << OOV identification >> . | USED-FOR | [
17,
17,
22,
23
] |
This paper presents a word segmentation system in France Telecom R&D Beijing , which uses a unified approach to [[ word breaking ]] and << OOV identification >> . | CONJUNCTION | [
19,
20,
22,
23
] |
The system participated in all the tracks of the << segmentation bakeoff >> -- [[ PK-open ]] , PK-closed , AS-open , AS-closed , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks . | HYPONYM-OF | [
12,
12,
9,
10
] |
The system participated in all the tracks of the segmentation bakeoff -- [[ PK-open ]] , << PK-closed >> , AS-open , AS-closed , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks . | CONJUNCTION | [
12,
12,
14,
14
] |
The system participated in all the tracks of the << segmentation bakeoff >> -- PK-open , [[ PK-closed ]] , AS-open , AS-closed , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks . | HYPONYM-OF | [
14,
14,
9,
10
] |
The system participated in all the tracks of the segmentation bakeoff -- PK-open , [[ PK-closed ]] , << AS-open >> , AS-closed , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks . | CONJUNCTION | [
14,
14,
16,
16
] |
The system participated in all the tracks of the << segmentation bakeoff >> -- PK-open , PK-closed , [[ AS-open ]] , AS-closed , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks . | HYPONYM-OF | [
16,
16,
9,
10
] |
The system participated in all the tracks of the segmentation bakeoff -- PK-open , PK-closed , [[ AS-open ]] , << AS-closed >> , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks . | CONJUNCTION | [
16,
16,
18,
18
] |
The system participated in all the tracks of the << segmentation bakeoff >> -- PK-open , PK-closed , AS-open , [[ AS-closed ]] , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks . | HYPONYM-OF | [
18,
18,
9,
10
] |
The system participated in all the tracks of the segmentation bakeoff -- PK-open , PK-closed , AS-open , [[ AS-closed ]] , << HK-open >> , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks . | CONJUNCTION | [
18,
18,
20,
20
] |
The system participated in all the tracks of the << segmentation bakeoff >> -- PK-open , PK-closed , AS-open , AS-closed , [[ HK-open ]] , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks . | HYPONYM-OF | [
20,
20,
9,
10
] |
The system participated in all the tracks of the segmentation bakeoff -- PK-open , PK-closed , AS-open , AS-closed , [[ HK-open ]] , << HK-closed >> , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks . | CONJUNCTION | [
20,
20,
22,
22
] |
The system participated in all the tracks of the << segmentation bakeoff >> -- PK-open , PK-closed , AS-open , AS-closed , HK-open , [[ HK-closed ]] , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks . | HYPONYM-OF | [
22,
22,
9,
10
] |
The system participated in all the tracks of the segmentation bakeoff -- PK-open , PK-closed , AS-open , AS-closed , HK-open , [[ HK-closed ]] , << MSR-open >> and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks . | CONJUNCTION | [
22,
22,
24,
24
] |
The system participated in all the tracks of the << segmentation bakeoff >> -- PK-open , PK-closed , AS-open , AS-closed , HK-open , HK-closed , [[ MSR-open ]] and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks . | HYPONYM-OF | [
24,
24,
9,
10
] |
The system participated in all the tracks of the segmentation bakeoff -- PK-open , PK-closed , AS-open , AS-closed , HK-open , HK-closed , [[ MSR-open ]] and << MSR - closed >> -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks . | CONJUNCTION | [
24,
24,
26,
28
] |
The system participated in all the tracks of the << segmentation bakeoff >> -- PK-open , PK-closed , AS-open , AS-closed , HK-open , HK-closed , MSR-open and [[ MSR - closed ]] -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks . | HYPONYM-OF | [
26,
28,
9,
10
] |
The system participated in all the tracks of the segmentation bakeoff -- PK-open , PK-closed , AS-open , AS-closed , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in [[ MSR-open ]] , << MSR-close >> and PK-open tracks . | CONJUNCTION | [
36,
36,
38,
38
] |
The system participated in all the tracks of the segmentation bakeoff -- PK-open , PK-closed , AS-open , AS-closed , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , [[ MSR-close ]] and << PK-open >> tracks . | CONJUNCTION | [
38,
38,
40,
40
] |
This paper describes a [[ method ]] of << interactively visualizing and directing the process of translating >> a sentence . | USED-FOR | [
4,
4,
6,
13
] |
The [[ method ]] allows a user to explore a << model >> of syntax-based statistical machine translation -LRB- MT -RRB- , to understand the model 's strengths and weaknesses , and to compare it to other MT systems . | USED-FOR | [
1,
1,
8,
8
] |
The method allows a user to explore a [[ model ]] of << syntax-based statistical machine translation -LRB- MT -RRB- >> , to understand the model 's strengths and weaknesses , and to compare it to other MT systems . | USED-FOR | [
8,
8,
10,
16
] |
The method allows a user to explore a model of syntax-based statistical machine translation -LRB- MT -RRB- , to understand the model 's strengths and weaknesses , and to compare [[ it ]] to other << MT systems >> . | COMPARE | [
30,
30,
33,
34
] |
Using this [[ visualization method ]] , we can find and address conceptual and practical problems in an << MT system >> . | USED-FOR | [
2,
3,
16,
17
] |
A [[ method ]] of << sense resolution >> is proposed that is based on WordNet , an on-line lexical database that incorporates semantic relations -LRB- synonymy , antonymy , hyponymy , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses . | USED-FOR | [
1,
1,
3,
4
] |
A << method >> of sense resolution is proposed that is based on [[ WordNet ]] , an on-line lexical database that incorporates semantic relations -LRB- synonymy , antonymy , hyponymy , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses . | USED-FOR | [
11,
11,
1,
1
] |
A method of sense resolution is proposed that is based on [[ WordNet ]] , an << on-line lexical database >> that incorporates semantic relations -LRB- synonymy , antonymy , hyponymy , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses . | HYPONYM-OF | [
11,
11,
14,
16
] |
A method of sense resolution is proposed that is based on << WordNet >> , an on-line lexical database that incorporates [[ semantic relations ]] -LRB- synonymy , antonymy , hyponymy , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses . | PART-OF | [
19,
20,
11,
11
] |
A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates << semantic relations >> -LRB- [[ synonymy ]] , antonymy , hyponymy , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses . | HYPONYM-OF | [
22,
22,
19,
20
] |
A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates semantic relations -LRB- [[ synonymy ]] , << antonymy >> , hyponymy , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses . | CONJUNCTION | [
22,
22,
24,
24
] |
A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates << semantic relations >> -LRB- synonymy , [[ antonymy ]] , hyponymy , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses . | HYPONYM-OF | [
24,
24,
19,
20
] |
A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates semantic relations -LRB- synonymy , [[ antonymy ]] , << hyponymy >> , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses . | CONJUNCTION | [
24,
24,
26,
26
] |
A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates << semantic relations >> -LRB- synonymy , antonymy , [[ hyponymy ]] , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses . | HYPONYM-OF | [
26,
26,
19,
20
] |
A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates semantic relations -LRB- synonymy , antonymy , [[ hyponymy ]] , << meronymy >> , causal and troponymic entailment -RRB- as labeled pointers between word senses . | CONJUNCTION | [
26,
26,
28,
28
] |
A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates << semantic relations >> -LRB- synonymy , antonymy , hyponymy , [[ meronymy ]] , causal and troponymic entailment -RRB- as labeled pointers between word senses . | HYPONYM-OF | [
28,
28,
19,
20
] |
A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates semantic relations -LRB- synonymy , antonymy , hyponymy , [[ meronymy ]] , << causal and troponymic entailment >> -RRB- as labeled pointers between word senses . | CONJUNCTION | [
28,
28,
30,
33
] |
A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates << semantic relations >> -LRB- synonymy , antonymy , hyponymy , meronymy , [[ causal and troponymic entailment ]] -RRB- as labeled pointers between word senses . | HYPONYM-OF | [
30,
33,
19,
20
] |
With [[ WordNet ]] , it is easy to retrieve sets of << semantically related words >> , a facility that will be used for sense resolution during text processing , as follows . | USED-FOR | [
1,
1,
10,
12
] |
With WordNet , it is easy to retrieve sets of [[ semantically related words ]] , a facility that will be used for << sense resolution >> during text processing , as follows . | USED-FOR | [
10,
12,
21,
22
] |
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