Datasets:
yxchar
/
sciie-tlm

Dataset card Data Studio Files
xet
Community
Unnamed: 0
int64
0
3.22k
text
stringlengths
49
577
id
int64
0
3.22k
label
int64
0
6
0
The agreement in question involves number in [[ nouns ]] and << reflexive pronouns >> and is syntactic rather than semantic in nature because grammatical number in English , like grammatical gender in languages such as French , is partly arbitrary .
0
0
1
The agreement in question involves number in nouns and reflexive pronouns and is syntactic rather than semantic in nature because grammatical number in English , like [[ grammatical gender ]] in << languages >> such as French , is partly arbitrary .
1
1
2
The agreement in question involves number in nouns and reflexive pronouns and is syntactic rather than semantic in nature because grammatical number in English , like grammatical gender in << languages >> such as [[ French ]] , is partly arbitrary .
2
2
3
In this paper , a novel [[ method ]] to learn the << intrinsic object structure >> for robust visual tracking is proposed .
3
3
4
In this paper , a novel method to learn the [[ intrinsic object structure ]] for << robust visual tracking >> is proposed .
4
3
5
The basic assumption is that the << parameterized object state >> lies on a [[ low dimensional manifold ]] and can be learned from training data .
5
1
6
Based on this assumption , firstly we derived the [[ dimensionality reduction and density estimation algorithm ]] for << unsupervised learning of object intrinsic representation >> , the obtained non-rigid part of object state reduces even to 2 dimensions .
6
3
7
Secondly the << dynamical model >> is derived and trained based on this [[ intrinsic representation ]] .
7
3
8
Thirdly the learned [[ intrinsic object structure ]] is integrated into a << particle-filter style tracker >> .
8
4
9
We will show that this intrinsic object representation has some interesting properties and based on which the newly derived [[ dynamical model ]] makes << particle-filter style tracker >> more robust and reliable .
9
3
10
Experiments show that the learned [[ tracker ]] performs much better than existing << trackers >> on the tracking of complex non-rigid motions such as fish twisting with self-occlusion and large inter-frame lip motion .
10
5
11
Experiments show that the learned [[ tracker ]] performs much better than existing trackers on the << tracking of complex non-rigid motions >> such as fish twisting with self-occlusion and large inter-frame lip motion .
11
3
12
Experiments show that the learned tracker performs much better than existing [[ trackers ]] on the << tracking of complex non-rigid motions >> such as fish twisting with self-occlusion and large inter-frame lip motion .
12
3
13
Experiments show that the learned tracker performs much better than existing trackers on the tracking of << complex non-rigid motions >> such as [[ fish twisting ]] with self-occlusion and large inter-frame lip motion .
13
2
14
Experiments show that the learned tracker performs much better than existing trackers on the tracking of complex non-rigid motions such as << fish twisting >> with [[ self-occlusion ]] and large inter-frame lip motion .
14
1
15
Experiments show that the learned tracker performs much better than existing trackers on the tracking of complex non-rigid motions such as fish twisting with [[ self-occlusion ]] and large << inter-frame lip motion >> .
15
0
16
Experiments show that the learned tracker performs much better than existing trackers on the tracking of complex non-rigid motions such as << fish twisting >> with self-occlusion and large [[ inter-frame lip motion ]] .
16
1
17
The proposed [[ method ]] also has the potential to solve other type of << tracking problems >> .
17
3
18
In this paper , we present a [[ digital signal processor -LRB- DSP -RRB- implementation ]] of << real-time statistical voice conversion -LRB- VC -RRB- >> for silent speech enhancement and electrolaryngeal speech enhancement .
18
3
19
In this paper , we present a digital signal processor -LRB- DSP -RRB- implementation of [[ real-time statistical voice conversion -LRB- VC -RRB- ]] for << silent speech enhancement >> and electrolaryngeal speech enhancement .
19
3
20
In this paper , we present a digital signal processor -LRB- DSP -RRB- implementation of [[ real-time statistical voice conversion -LRB- VC -RRB- ]] for silent speech enhancement and << electrolaryngeal speech enhancement >> .
20
3
21
In this paper , we present a digital signal processor -LRB- DSP -RRB- implementation of real-time statistical voice conversion -LRB- VC -RRB- for [[ silent speech enhancement ]] and << electrolaryngeal speech enhancement >> .
21
0
22
[[ Electrolaryngeal speech ]] is one of the typical types of << alaryngeal speech >> produced by an alternative speaking method for laryngectomees .
22
2
23
Electrolaryngeal speech is one of the typical types of << alaryngeal speech >> produced by an alternative [[ speaking method ]] for laryngectomees .
23
3
24
Electrolaryngeal speech is one of the typical types of alaryngeal speech produced by an alternative [[ speaking method ]] for << laryngectomees >> .
24
3
25
However , the [[ sound quality ]] of << NAM and electrolaryngeal speech >> suffers from lack of naturalness .
25
6
26
VC has proven to be one of the promising approaches to address this problem , and << it >> has been successfully implemented on [[ devices ]] with sufficient computational resources .
26
3
27
VC has proven to be one of the promising approaches to address this problem , and it has been successfully implemented on << devices >> with [[ sufficient computational resources ]] .
27
1
28
An implementation on << devices >> that are highly portable but have [[ limited computational resources ]] would greatly contribute to its practical use .
28
1
29
In this paper we further implement << real-time VC >> on a [[ DSP ]] .
29
3
30
To implement the two << speech enhancement systems >> based on [[ real-time VC ]] , one from NAM to a whispered voice and the other from electrolaryngeal speech to a natural voice , we propose several methods for reducing computational cost while preserving conversion accuracy .
30
3
31
To implement the two << speech enhancement systems >> based on real-time VC , [[ one ]] from NAM to a whispered voice and the other from electrolaryngeal speech to a natural voice , we propose several methods for reducing computational cost while preserving conversion accuracy .
31
2
32
To implement the two speech enhancement systems based on real-time VC , [[ one ]] from NAM to a whispered voice and the << other >> from electrolaryngeal speech to a natural voice , we propose several methods for reducing computational cost while preserving conversion accuracy .
32
0
33
To implement the two << speech enhancement systems >> based on real-time VC , one from NAM to a whispered voice and the [[ other ]] from electrolaryngeal speech to a natural voice , we propose several methods for reducing computational cost while preserving conversion accuracy .
33
2
34
To implement the two speech enhancement systems based on real-time VC , one from NAM to a whispered voice and the other from electrolaryngeal speech to a natural voice , we propose several << methods >> for reducing [[ computational cost ]] while preserving conversion accuracy .
34
6
35
To implement the two speech enhancement systems based on real-time VC , one from NAM to a whispered voice and the other from electrolaryngeal speech to a natural voice , we propose several methods for reducing [[ computational cost ]] while preserving << conversion accuracy >> .
35
0
36
To implement the two speech enhancement systems based on real-time VC , one from NAM to a whispered voice and the other from electrolaryngeal speech to a natural voice , we propose several << methods >> for reducing computational cost while preserving [[ conversion accuracy ]] .
36
6
37
We conduct experimental evaluations and show that << real-time VC >> is capable of running on a [[ DSP ]] with little degradation .
37
3
38
We propose a [[ method ]] that automatically generates << paraphrase >> sets from seed sentences to be used as reference sets in objective machine translation evaluation measures like BLEU and NIST .
38
3
39
We propose a method that automatically generates [[ paraphrase ]] sets from seed sentences to be used as reference sets in objective << machine translation evaluation measures >> like BLEU and NIST .
39
3
40
We propose a method that automatically generates paraphrase sets from seed sentences to be used as reference sets in objective << machine translation evaluation measures >> like [[ BLEU ]] and NIST .
40
2
41
We propose a method that automatically generates paraphrase sets from seed sentences to be used as reference sets in objective machine translation evaluation measures like [[ BLEU ]] and << NIST >> .
41
0
42
We propose a method that automatically generates paraphrase sets from seed sentences to be used as reference sets in objective << machine translation evaluation measures >> like BLEU and [[ NIST ]] .
42
2
43
We measured the quality of the paraphrases produced in an experiment , i.e. , -LRB- i -RRB- their << grammaticality >> : at least 99 % correct sentences ; -LRB- ii -RRB- their [[ equivalence in meaning ]] : at least 96 % correct paraphrases either by meaning equivalence or entailment ; and , -LRB- iii -RRB- the amount of internal lexical and syntactical variation in a set of paraphrases : slightly superior to that of hand-produced sets .
43
0
44
We measured the quality of the paraphrases produced in an experiment , i.e. , -LRB- i -RRB- their grammaticality : at least 99 % correct sentences ; -LRB- ii -RRB- their equivalence in meaning : at least 96 % correct << paraphrases >> either by [[ meaning equivalence ]] or entailment ; and , -LRB- iii -RRB- the amount of internal lexical and syntactical variation in a set of paraphrases : slightly superior to that of hand-produced sets .
44
3
45
We measured the quality of the paraphrases produced in an experiment , i.e. , -LRB- i -RRB- their grammaticality : at least 99 % correct sentences ; -LRB- ii -RRB- their equivalence in meaning : at least 96 % correct paraphrases either by [[ meaning equivalence ]] or << entailment >> ; and , -LRB- iii -RRB- the amount of internal lexical and syntactical variation in a set of paraphrases : slightly superior to that of hand-produced sets .
45
0
46
We measured the quality of the paraphrases produced in an experiment , i.e. , -LRB- i -RRB- their grammaticality : at least 99 % correct sentences ; -LRB- ii -RRB- their equivalence in meaning : at least 96 % correct << paraphrases >> either by meaning equivalence or [[ entailment ]] ; and , -LRB- iii -RRB- the amount of internal lexical and syntactical variation in a set of paraphrases : slightly superior to that of hand-produced sets .
46
3
47
We measured the quality of the paraphrases produced in an experiment , i.e. , -LRB- i -RRB- their grammaticality : at least 99 % correct sentences ; -LRB- ii -RRB- their << equivalence in meaning >> : at least 96 % correct paraphrases either by meaning equivalence or entailment ; and , -LRB- iii -RRB- the amount of [[ internal lexical and syntactical variation ]] in a set of paraphrases : slightly superior to that of hand-produced sets .
47
0
48
We measured the quality of the paraphrases produced in an experiment , i.e. , -LRB- i -RRB- their grammaticality : at least 99 % correct sentences ; -LRB- ii -RRB- their equivalence in meaning : at least 96 % correct paraphrases either by meaning equivalence or entailment ; and , -LRB- iii -RRB- the amount of internal lexical and syntactical variation in a set of [[ paraphrases ]] : slightly superior to that of << hand-produced sets >> .
48
5
49
The << paraphrase >> sets produced by this [[ method ]] thus seem adequate as reference sets to be used for MT evaluation .
49
3
50
[[ Graph unification ]] remains the most expensive part of << unification-based grammar parsing >> .
50
4
51
We focus on one [[ speed-up element ]] in the design of << unification algorithms >> : avoidance of copying of unmodified subgraphs .
51
4
52
We propose a << method >> of attaining such a design through a method of [[ structure-sharing ]] which avoids log -LRB- d -RRB- overheads often associated with structure-sharing of graphs without any use of costly dependency pointers .
52
3
53
The proposed [[ scheme ]] eliminates redundant copying while maintaining the quasi-destructive scheme 's ability to avoid over copying and early copying combined with its ability to handle << cyclic structures >> without algorithmic additions .
53
3
54
The proposed << scheme >> eliminates redundant copying while maintaining the [[ quasi-destructive scheme 's ability ]] to avoid over copying and early copying combined with its ability to handle cyclic structures without algorithmic additions .
54
1
55
The proposed scheme eliminates redundant copying while maintaining the quasi-destructive scheme 's ability to avoid [[ over copying ]] and << early copying >> combined with its ability to handle cyclic structures without algorithmic additions .
55
0
56
We describe a novel technique and implemented [[ system ]] for constructing a << subcategorization dictionary >> from textual corpora .
56
3
57
We describe a novel technique and implemented << system >> for constructing a subcategorization dictionary from [[ textual corpora ]] .
57
3
58
We also demonstrate that a << subcategorization dictionary >> built with the [[ system ]] improves the accuracy of a parser by an appreciable amount
58
3
59
We also demonstrate that a subcategorization dictionary built with the system improves the [[ accuracy ]] of a << parser >> by an appreciable amount
59
6
60
We also demonstrate that a << subcategorization dictionary >> built with the system improves the accuracy of a [[ parser ]] by an appreciable amount
60
6
61
A number of powerful << registration criteria >> have been developed in the last decade , most prominently the criterion of [[ maximum mutual information ]] .
61
2
62
Although this criterion provides for good registration results in many applications , << it >> remains a purely [[ low-level criterion ]] .
62
1
63
In this paper , we will develop a [[ Bayesian framework ]] that allows to impose statistically learned prior knowledge about the joint intensity distribution into << image registration methods >> .
63
3
64
In this paper , we will develop a Bayesian framework that allows to impose [[ statistically learned prior knowledge ]] about the joint intensity distribution into << image registration methods >> .
64
3
65
In this paper , we will develop a Bayesian framework that allows to impose << statistically learned prior knowledge >> about the [[ joint intensity distribution ]] into image registration methods .
65
1
66
The << prior >> is given by a [[ kernel density estimate ]] on the space of joint intensity distributions computed from a representative set of pre-registered image pairs .
66
3
67
The prior is given by a [[ kernel density estimate ]] on the space of << joint intensity distributions >> computed from a representative set of pre-registered image pairs .
67
3
68
The prior is given by a kernel density estimate on the space of << joint intensity distributions >> computed from a representative set of [[ pre-registered image pairs ]] .
68
3
69
Experimental results demonstrate that the resulting [[ registration process ]] is more robust to << missing low-level information >> as it favors intensity correspondences statistically consistent with the learned intensity distributions .
69
3
70
Experimental results demonstrate that the resulting registration process is more robust to missing low-level information as [[ it ]] favors << intensity correspondences >> statistically consistent with the learned intensity distributions .
70
3
71
We present a [[ method ]] for << synthesizing complex , photo-realistic facade images >> , from a single example .
71
3
72
After parsing the example image into its << semantic components >> , a [[ tiling ]] for it is generated .
72
3
73
Novel tilings can then be created , yielding << facade textures >> with different dimensions or with [[ occluded parts inpainted ]] .
73
1
74
A [[ genetic algorithm ]] guides the novel << facades >> as well as inpainted parts to be consistent with the example , both in terms of their overall structure and their detailed textures .
74
3
75
A [[ genetic algorithm ]] guides the novel facades as well as << inpainted parts >> to be consistent with the example , both in terms of their overall structure and their detailed textures .
75
3
76
Promising results for [[ multiple standard datasets ]] -- in particular for the different building styles they contain -- demonstrate the potential of the << method >> .
76
6
77
We introduce a new << interactive corpus exploration tool >> called [[ InfoMagnets ]] .
77
2
78
[[ InfoMagnets ]] aims at making << exploratory corpus analysis >> accessible to researchers who are not experts in text mining .
78
3
79
As evidence of its usefulness and usability , [[ it ]] has been used successfully in a research context to uncover relationships between language and behavioral patterns in two distinct << domains >> : tutorial dialogue -LRB- Kumar et al. , submitted -RRB- and on-line communities -LRB- Arguello et al. , 2006 -RRB- .
79
3
80
As evidence of its usefulness and usability , it has been used successfully in a research context to uncover relationships between language and behavioral patterns in two distinct << domains >> : [[ tutorial dialogue ]] -LRB- Kumar et al. , submitted -RRB- and on-line communities -LRB- Arguello et al. , 2006 -RRB- .
80
2
81
As evidence of its usefulness and usability , it has been used successfully in a research context to uncover relationships between language and behavioral patterns in two distinct domains : [[ tutorial dialogue ]] -LRB- Kumar et al. , submitted -RRB- and << on-line communities >> -LRB- Arguello et al. , 2006 -RRB- .
81
0
82
As evidence of its usefulness and usability , it has been used successfully in a research context to uncover relationships between language and behavioral patterns in two distinct << domains >> : tutorial dialogue -LRB- Kumar et al. , submitted -RRB- and [[ on-line communities ]] -LRB- Arguello et al. , 2006 -RRB- .
82
2
83
As an [[ educational tool ]] , it has been used as part of a unit on << protocol analysis >> in an Educational Research Methods course .
83
3
84
Sources of training data suitable for << language modeling >> of [[ conversational speech ]] are limited .
84
3
85
In this paper , we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target << recognition task >> , but also that it is possible to get bigger performance gains from the data by using [[ class-dependent interpolation of N-grams ]] .
85
3
86
We present a [[ method ]] for << detecting 3D objects >> using multi-modalities .
86
3
87
We present a << method >> for detecting 3D objects using [[ multi-modalities ]] .
87
3
88
While [[ it ]] is generic , we demonstrate << it >> on the combination of an image and a dense depth map which give complementary object information .
88
3
89
While it is generic , we demonstrate << it >> on the combination of an [[ image ]] and a dense depth map which give complementary object information .
89
3
90
While it is generic , we demonstrate it on the combination of an [[ image ]] and a << dense depth map >> which give complementary object information .
90
0
91
While it is generic , we demonstrate << it >> on the combination of an image and a [[ dense depth map ]] which give complementary object information .
91
3
92
While it is generic , we demonstrate it on the combination of an image and a << dense depth map >> which give [[ complementary object information ]] .
92
1
93
It is based on an efficient representation of [[ templates ]] that capture the different << modalities >> , and we show in many experiments on commodity hardware that our approach significantly outperforms state-of-the-art methods on single modalities .
93
3
94
It is based on an efficient representation of templates that capture the different modalities , and we show in many experiments on commodity hardware that our [[ approach ]] significantly outperforms << state-of-the-art methods >> on single modalities .
94
5
95
It is based on an efficient representation of templates that capture the different modalities , and we show in many experiments on commodity hardware that our [[ approach ]] significantly outperforms state-of-the-art methods on << single modalities >> .
95
3
96
It is based on an efficient representation of templates that capture the different modalities , and we show in many experiments on commodity hardware that our approach significantly outperforms [[ state-of-the-art methods ]] on << single modalities >> .
96
3
97
The [[ compact description of a video sequence ]] through a single image map and a dominant motion has applications in several << domains >> , including video browsing and retrieval , compression , mosaicing , and visual summarization .
97
3
98
The << compact description of a video sequence >> through a single [[ image map ]] and a dominant motion has applications in several domains , including video browsing and retrieval , compression , mosaicing , and visual summarization .
98
3
99
The compact description of a video sequence through a single [[ image map ]] and a << dominant motion >> has applications in several domains , including video browsing and retrieval , compression , mosaicing , and visual summarization .
99
0