mneb/sci-erc · Datasets at Hugging Face

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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 .	{ "relations": { "conjunction": [ { "head": { "text": "nouns", "start": 45, "end": 50 }, "tail": { "text": "reflexive pronouns", "start": 55, "end": 73 } } ], "feature of": [ { "head": { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
In this paper , a novel method to learn the intrinsic object structure for robust visual tracking is proposed .	{ "relations": { "used for": [ { "head": { "text": "method", "start": 24, "end": 30 }, "tail": { "text": "intrinsic object structure", "start": 44, "end": 70 } }, { "head": { "text": "in...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
The basic assumption is that the parameterized object state lies on a low dimensional manifold and can be learned from training data .	{ "relations": { "feature of": [ { "head": { "text": "low dimensional manifold", "start": 70, "end": 94 }, "tail": { "text": "parameterized object state", "start": 33, "end": 59 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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 .	{ "relations": { "used for": [ { "head": { "text": "dimensionality reduction and density estimation algorithm", "start": 50, "end": 107 }, "tail": { "text": "unsupervised learning of object intrinsic representation", "start": 112, ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Secondly the dynamical model is derived and trained based on this intrinsic representation .	{ "relations": { "used for": [ { "head": { "text": "intrinsic representation", "start": 66, "end": 90 }, "tail": { "text": "dynamical model", "start": 13, "end": 28 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Thirdly the learned intrinsic object structure is integrated into a particle-filter style tracker .	{ "relations": { "part of": [ { "head": { "text": "intrinsic object structure", "start": 20, "end": 46 }, "tail": { "text": "particle-filter style tracker", "start": 68, "end": 97 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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 .	{ "relations": { "used for": [ { "head": { "text": "dynamical model", "start": 124, "end": 139 }, "tail": { "text": "particle-filter style tracker", "start": 146, "end": 175 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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 .	{ "relations": { "compare": [ { "head": { "text": "tracker", "start": 34, "end": 41 }, "tail": { "text": "trackers", "start": 77, "end": 85 } } ], "used for": [ { "head": { "text...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
The proposed method also has the potential to solve other type of tracking problems .	{ "relations": { "used for": [ { "head": { "text": "method", "start": 13, "end": 19 }, "tail": { "text": "tracking problems", "start": 66, "end": 83 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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 .	{ "relations": { "used for": [ { "head": { "text": "digital signal processor -LRB- DSP -RRB- implementation", "start": 29, "end": 84 }, "tail": { "text": "real-time statistical voice conversion -LRB- VC -RRB-", "start": 88, ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Electrolaryngeal speech is one of the typical types of alaryngeal speech produced by an alternative speaking method for laryngectomees .	{ "relations": { "hyponym of": [ { "head": { "text": "Electrolaryngeal speech", "start": 0, "end": 23 }, "tail": { "text": "alaryngeal speech", "start": 55, "end": 72 } } ], "used for": [ { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
However , the sound quality of NAM and electrolaryngeal speech suffers from lack of naturalness .	{ "relations": { "evaluate for": [ { "head": { "text": "sound quality", "start": 14, "end": 27 }, "tail": { "text": "NAM and electrolaryngeal speech", "start": 31, "end": 62 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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 .	{ "relations": { "used for": [ { "head": { "text": "devices", "start": 122, "end": 129 }, "tail": { "text": "it", "start": 82, "end": 84 } } ], "feature of": [ { "head": { "text"...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
An implementation on devices that are highly portable but have limited computational resources would greatly contribute to its practical use .	{ "relations": { "feature of": [ { "head": { "text": "limited computational resources", "start": 63, "end": 94 }, "tail": { "text": "devices", "start": 21, "end": 28 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
In this paper we further implement real-time VC on a DSP .	{ "relations": { "used for": [ { "head": { "text": "DSP", "start": 53, "end": 56 }, "tail": { "text": "real-time VC", "start": 35, "end": 47 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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 .	{ "relations": { "used for": [ { "head": { "text": "real-time VC", "start": 57, "end": 69 }, "tail": { "text": "speech enhancement systems", "start": 21, "end": 47 } } ], "hyponym of": [ { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We conduct experimental evaluations and show that real-time VC is capable of running on a DSP with little degradation .	{ "relations": { "used for": [ { "head": { "text": "DSP", "start": 90, "end": 93 }, "tail": { "text": "real-time VC", "start": 50, "end": 62 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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 .	{ "relations": { "used for": [ { "head": { "text": "method", "start": 13, "end": 19 }, "tail": { "text": "paraphrase", "start": 49, "end": 59 } }, { "head": { "text": "paraphrase", ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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 ...	{ "relations": { "conjunction": [ { "head": { "text": "equivalence in meaning", "start": 170, "end": 192 }, "tail": { "text": "grammaticality", "start": 98, "end": 112 } }, { "head": { "...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
The paraphrase sets produced by this method thus seem adequate as reference sets to be used for MT evaluation .	{ "relations": { "used for": [ { "head": { "text": "method", "start": 37, "end": 43 }, "tail": { "text": "paraphrase", "start": 4, "end": 14 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Graph unification remains the most expensive part of unification-based grammar parsing .	{ "relations": { "part of": [ { "head": { "text": "Graph unification", "start": 0, "end": 17 }, "tail": { "text": "unification-based grammar parsing", "start": 53, "end": 86 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We focus on one speed-up element in the design of unification algorithms : avoidance of copying of unmodified subgraphs .	{ "relations": { "part of": [ { "head": { "text": "speed-up element", "start": 16, "end": 32 }, "tail": { "text": "unification algorithms", "start": 50, "end": 72 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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 .	{ "relations": { "used for": [ { "head": { "text": "structure-sharing", "start": 67, "end": 84 }, "tail": { "text": "method", "start": 13, "end": 19 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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 .	{ "relations": { "used for": [ { "head": { "text": "scheme", "start": 13, "end": 19 }, "tail": { "text": "cyclic structures", "start": 183, "end": 200 } } ], "feature of": [ { "head": { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We describe a novel technique and implemented system for constructing a subcategorization dictionary from textual corpora .	{ "relations": { "used for": [ { "head": { "text": "system", "start": 46, "end": 52 }, "tail": { "text": "subcategorization dictionary", "start": 72, "end": 100 } }, { "head": { "text": ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We also demonstrate that a subcategorization dictionary built with the system improves the accuracy of a parser by an appreciable amount	{ "relations": { "used for": [ { "head": { "text": "system", "start": 71, "end": 77 }, "tail": { "text": "subcategorization dictionary", "start": 27, "end": 55 } } ], "evaluate for": [ { "...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
A number of powerful registration criteria have been developed in the last decade , most prominently the criterion of maximum mutual information .	{ "relations": { "hyponym of": [ { "head": { "text": "maximum mutual information", "start": 118, "end": 144 }, "tail": { "text": "registration criteria", "start": 21, "end": 42 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Although this criterion provides for good registration results in many applications , it remains a purely low-level criterion .	{ "relations": { "feature of": [ { "head": { "text": "low-level criterion", "start": 106, "end": 125 }, "tail": { "text": "it", "start": 16, "end": 18 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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 .	{ "relations": { "used for": [ { "head": { "text": "Bayesian framework", "start": 34, "end": 52 }, "tail": { "text": "image registration methods", "start": 157, "end": 183 } }, { "head": { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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 .	{ "relations": { "used for": [ { "head": { "text": "kernel density estimate", "start": 24, "end": 47 }, "tail": { "text": "prior", "start": 4, "end": 9 } }, { "head": { "text": "kernel d...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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 .	{ "relations": { "used for": [ { "head": { "text": "registration process", "start": 52, "end": 72 }, "tail": { "text": "missing low-level information", "start": 91, "end": 120 } }, { "head": { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We present a method for synthesizing complex , photo-realistic facade images , from a single example .	{ "relations": { "used for": [ { "head": { "text": "method", "start": 13, "end": 19 }, "tail": { "text": "synthesizing complex , photo-realistic facade images", "start": 24, "end": 76 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
After parsing the example image into its semantic components , a tiling for it is generated .	{ "relations": { "used for": [ { "head": { "text": "tiling", "start": 65, "end": 71 }, "tail": { "text": "semantic components", "start": 41, "end": 60 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Novel tilings can then be created , yielding facade textures with different dimensions or with occluded parts inpainted .	{ "relations": { "feature of": [ { "head": { "text": "occluded parts inpainted", "start": 95, "end": 119 }, "tail": { "text": "facade textures", "start": 45, "end": 60 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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 .	{ "relations": { "used for": [ { "head": { "text": "genetic algorithm", "start": 2, "end": 19 }, "tail": { "text": "facades", "start": 37, "end": 44 } }, { "head": { "text": "genetic alg...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Promising results for multiple standard datasets -- in particular for the different building styles they contain -- demonstrate the potential of the method .	{ "relations": { "evaluate for": [ { "head": { "text": "multiple standard datasets", "start": 22, "end": 48 }, "tail": { "text": "method", "start": 149, "end": 155 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We introduce a new interactive corpus exploration tool called InfoMagnets .	{ "relations": { "hyponym of": [ { "head": { "text": "InfoMagnets", "start": 62, "end": 73 }, "tail": { "text": "interactive corpus exploration tool", "start": 19, "end": 54 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
InfoMagnets aims at making exploratory corpus analysis accessible to researchers who are not experts in text mining .	{ "relations": { "used for": [ { "head": { "text": "InfoMagnets", "start": 0, "end": 11 }, "tail": { "text": "exploratory corpus analysis", "start": 27, "end": 54 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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- .	{ "relations": { "used for": [ { "head": { "text": "it", "start": 15, "end": 17 }, "tail": { "text": "domains", "start": 180, "end": 187 } } ], "hyponym of": [ { "head": { "text"...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
As an educational tool , it has been used as part of a unit on protocol analysis in an Educational Research Methods course .	{ "relations": { "used for": [ { "head": { "text": "educational tool", "start": 6, "end": 22 }, "tail": { "text": "protocol analysis", "start": 63, "end": 80 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Sources of training data suitable for language modeling of conversational speech are limited .	{ "relations": { "used for": [ { "head": { "text": "conversational speech", "start": 59, "end": 80 }, "tail": { "text": "language modeling", "start": 38, "end": 55 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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 .	{ "relations": { "used for": [ { "head": { "text": "class-dependent interpolation of N-grams", "start": 243, "end": 283 }, "tail": { "text": "recognition task", "start": 140, "end": 156 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We present a method for detecting 3D objects using multi-modalities .	{ "relations": { "used for": [ { "head": { "text": "method", "start": 13, "end": 19 }, "tail": { "text": "detecting 3D objects", "start": 24, "end": 44 } }, { "head": { "text": "multi-mo...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
While it is generic , we demonstrate it on the combination of an image and a dense depth map which give complementary object information .	{ "relations": { "used for": [ { "head": { "text": "it", "start": 6, "end": 8 }, "tail": { "text": "it", "start": 6, "end": 8 } }, { "head": { "text": "image", "start": 65, ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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 .	{ "relations": { "used for": [ { "head": { "text": "templates", "start": 46, "end": 55 }, "tail": { "text": "modalities", "start": 83, "end": 93 } }, { "head": { "text": "approach", ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
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 .	{ "relations": { "used for": [ { "head": { "text": "compact description of a video sequence", "start": 4, "end": 43 }, "tail": { "text": "domains", "start": 121, "end": 128 } }, { "head": { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Building such a representation requires the capability to register all the frames with respect to the dominant object in the scene , a task which has been , in the past , addressed through temporally localized motion estimates .	{ "relations": { "used for": [ { "head": { "text": "localized motion estimates", "start": 200, "end": 226 }, "tail": { "text": "task", "start": 135, "end": 139 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
To avoid this oscillation , we augment the motion model with a generic temporal constraint which increases the robustness against competing interpretations , leading to more meaningful content summarization .	{ "relations": { "used for": [ { "head": { "text": "generic temporal constraint", "start": 63, "end": 90 }, "tail": { "text": "motion model", "start": 43, "end": 55 } }, { "head": { "tex...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
In cross-domain learning , there is a more challenging problem that the domain divergence involves more than one dominant factors , e.g. , different viewpoints , various resolutions and changing illuminations .	{ "relations": { "part of": [ { "head": { "text": "dominant factors", "start": 113, "end": 129 }, "tail": { "text": "domain divergence", "start": 72, "end": 89 } } ], "hyponym of": [ { "he...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Fortunately , an intermediate domain could often be found to build a bridge across them to facilitate the learning problem .	{ "relations": { "used for": [ { "head": { "text": "intermediate domain", "start": 17, "end": 36 }, "tail": { "text": "learning problem", "start": 106, "end": 122 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
In this paper , we propose a Coupled Marginalized Denoising Auto-encoders framework to address the cross-domain problem .	{ "relations": { "used for": [ { "head": { "text": "Coupled Marginalized Denoising Auto-encoders framework", "start": 29, "end": 83 }, "tail": { "text": "cross-domain problem", "start": 99, "end": 119 } } ]...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Specifically , we design two marginalized denoising auto-encoders , one for the target and the other for source as well as the intermediate one .	{ "relations": { "hyponym of": [ { "head": { "text": "one", "start": 68, "end": 71 }, "tail": { "text": "marginalized denoising auto-encoders", "start": 29, "end": 65 } }, { "head": { "t...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
To better couple the two denoising auto-encoders learning , we incorporate a feature mapping , which tends to transfer knowledge between the intermediate domain and the target one .	{ "relations": { "part of": [ { "head": { "text": "feature mapping", "start": 77, "end": 92 }, "tail": { "text": "denoising auto-encoders learning", "start": 25, "end": 57 } } ], "used for": [ { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Furthermore , the maximum margin criterion , e.g. , intra-class com-pactness and inter-class penalty , on the output layer is imposed to seek more discriminative features across different domains .	{ "relations": { "hyponym of": [ { "head": { "text": "intra-class com-pactness", "start": 52, "end": 76 }, "tail": { "text": "maximum margin criterion", "start": 18, "end": 42 } }, { "head": { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Extensive experiments on two tasks have demonstrated the superiority of our method over the state-of-the-art methods .	{ "relations": { "evaluate for": [ { "head": { "text": "tasks", "start": 29, "end": 34 }, "tail": { "text": "method", "start": 76, "end": 82 } } ], "compare": [ { "head": { "text...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Basically , a set of age-group specific dictionaries are learned , where the dictionary bases corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups , and a linear combination of these patterns expresses a particular personalized aging process ...	{ "relations": { "part of": [ { "head": { "text": "dictionary bases", "start": 77, "end": 93 }, "tail": { "text": "age-group specific dictionaries", "start": 21, "end": 52 } } ], "used for": [ { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
First , beyond the aging dictionaries , each subject may have extra personalized facial characteristics , e.g. mole , which are invariant in the aging process .	{ "relations": { "hyponym of": [ { "head": { "text": "mole", "start": 111, "end": 115 }, "tail": { "text": "personalized facial characteristics", "start": 68, "end": 103 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Thus a personality-aware coupled reconstruction loss is utilized to learn the dictionaries based on face pairs from neighboring age groups .	{ "relations": { "used for": [ { "head": { "text": "personality-aware coupled reconstruction loss", "start": 7, "end": 52 }, "tail": { "text": "dictionaries", "start": 78, "end": 90 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Extensive experiments well demonstrate the advantages of our proposed solution over other state-of-the-arts in term of personalized aging progression , as well as the performance gain for cross-age face verification by synthesizing aging faces .	{ "relations": { "compare": [ { "head": { "text": "solution", "start": 70, "end": 78 }, "tail": { "text": "state-of-the-arts", "start": 90, "end": 107 } } ], "used for": [ { "head": { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We propose a draft scheme of the model formalizing the structure of communicative context in dialogue interaction .	{ "relations": { "used for": [ { "head": { "text": "model", "start": 33, "end": 38 }, "tail": { "text": "structure of communicative context", "start": 55, "end": 89 } } ], "feature of": [ { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Visitors who browse the web from wireless PDAs , cell phones , and pagers are frequently stymied by web interfaces optimized for desktop PCs .	{ "relations": { "used for": [ { "head": { "text": "web interfaces", "start": 100, "end": 114 }, "tail": { "text": "desktop PCs", "start": 129, "end": 140 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
In this paper we develop an algorithm , MINPATH , that automatically improves wireless web navigation by suggesting useful shortcut links in real time .	{ "relations": { "used for": [ { "head": { "text": "algorithm", "start": 28, "end": 37 }, "tail": { "text": "wireless web navigation", "start": 78, "end": 101 } }, { "head": { "text": "a...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
MINPATH finds shortcuts by using a learned model of web visitor behavior to estimate the savings of shortcut links , and suggests only the few best links .	{ "relations": { "used for": [ { "head": { "text": "model", "start": 43, "end": 48 }, "tail": { "text": "MINPATH", "start": 0, "end": 7 } }, { "head": { "text": "model", "start...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We explore a variety of predictive models , including Na ¨ ıve Bayes mixture models and mixtures of Markov models , and report empirical evidence that MINPATH finds useful shortcuts that save substantial navigational effort .	{ "relations": { "hyponym of": [ { "head": { "text": "Na ¨ ıve Bayes mixture models", "start": 54, "end": 83 }, "tail": { "text": "predictive models", "start": 24, "end": 41 } }, { "head": { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
This paper describes a particular approach to parsing that utilizes recent advances in unification-based parsing and in classification-based knowledge representation .	{ "relations": { "used for": [ { "head": { "text": "approach", "start": 34, "end": 42 }, "tail": { "text": "parsing", "start": 46, "end": 53 } }, { "head": { "text": "unification-based p...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
As unification-based grammatical frameworks are extended to handle richer descriptions of linguistic information , they begin to share many of the properties that have been developed in KL-ONE-like knowledge representation systems .	{ "relations": { "used for": [ { "head": { "text": "unification-based grammatical frameworks", "start": 3, "end": 43 }, "tail": { "text": "linguistic information", "start": 90, "end": 112 } }, { "...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
This commonality suggests that some of the classification-based representation techniques can be applied to unification-based linguistic descriptions .	{ "relations": { "used for": [ { "head": { "text": "classification-based representation techniques", "start": 43, "end": 89 }, "tail": { "text": "unification-based linguistic descriptions", "start": 108, "end": 149 }...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
This merging supports the integration of semantic and syntactic information into the same system , simultaneously subject to the same types of processes , in an efficient manner .	{ "relations": { "used for": [ { "head": { "text": "semantic and syntactic information", "start": 41, "end": 75 }, "tail": { "text": "system", "start": 90, "end": 96 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
The use of a KL-ONE style representation for parsing and semantic interpretation was first explored in the PSI-KLONE system -LSB- 2 -RSB- , in which parsing is characterized as an inference process called incremental description refinement .	{ "relations": { "used for": [ { "head": { "text": "KL-ONE style representation", "start": 13, "end": 40 }, "tail": { "text": "parsing", "start": 45, "end": 52 } }, { "head": { "text": "...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
In this paper we discuss a proposed user knowledge modeling architecture for the ICICLE system , a language tutoring application for deaf learners of written English .	{ "relations": { "used for": [ { "head": { "text": "user knowledge modeling architecture", "start": 36, "end": 72 }, "tail": { "text": "ICICLE system", "start": 81, "end": 94 } }, { "head": { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
The model will represent the language proficiency of the user and is designed to be referenced during both writing analysis and feedback production .	{ "relations": { "used for": [ { "head": { "text": "model", "start": 4, "end": 9 }, "tail": { "text": "writing analysis", "start": 107, "end": 123 } }, { "head": { "text": "model", ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We motivate our model design by citing relevant research on second language and cognitive skill acquisition , and briefly discuss preliminary empirical evidence supporting the design .	{ "relations": { "used for": [ { "head": { "text": "second language and cognitive skill acquisition", "start": 60, "end": 107 }, "tail": { "text": "model design", "start": 16, "end": 28 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We conclude by showing how our design can provide a rich and robust information base to a language assessment / correction application by modeling user proficiency at a high level of granularity and specificity .	{ "relations": { "used for": [ { "head": { "text": "design", "start": 31, "end": 37 }, "tail": { "text": "language assessment / correction application", "start": 90, "end": 134 } }, { "head": { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Constraint propagation is one of the key techniques in constraint programming , and a large body of work has built up around it .	{ "relations": { "part of": [ { "head": { "text": "Constraint propagation", "start": 0, "end": 22 }, "tail": { "text": "constraint programming", "start": 55, "end": 77 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
In this paper we present SHORTSTR2 , a development of the Simple Tabular Reduction algorithm STR2 + .	{ "relations": { "used for": [ { "head": { "text": "Simple Tabular Reduction algorithm STR2 +", "start": 58, "end": 99 }, "tail": { "text": "SHORTSTR2", "start": 25, "end": 34 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We show that SHORTSTR2 is complementary to the existing algorithms SHORTGAC and HAGGISGAC that exploit short supports , while being much simpler .	{ "relations": { "compare": [ { "head": { "text": "SHORTSTR2", "start": 13, "end": 22 }, "tail": { "text": "SHORTGAC", "start": 67, "end": 75 } }, { "head": { "text": "SHORTSTR2", ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
When a constraint is amenable to short supports , the short support set can be exponentially smaller than the full-length support set .	{ "relations": { "compare": [ { "head": { "text": "short support set", "start": 54, "end": 71 }, "tail": { "text": "full-length support set", "start": 110, "end": 133 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We also show that SHORTSTR2 can be combined with a simple algorithm to identify short supports from full-length supports , to provide a superior drop-in replacement for STR2 + .	{ "relations": { "used for": [ { "head": { "text": "SHORTSTR2", "start": 18, "end": 27 }, "tail": { "text": "short supports", "start": 80, "end": 94 } }, { "head": { "text": "SHORTSTR2",...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We propose a detection method for orthographic variants caused by transliteration in a large corpus .	{ "relations": { "used for": [ { "head": { "text": "detection method", "start": 13, "end": 29 }, "tail": { "text": "orthographic variants", "start": 34, "end": 55 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
The method employs two similarities .	{ "relations": { "used for": [ { "head": { "text": "similarities", "start": 23, "end": 35 }, "tail": { "text": "method", "start": 4, "end": 10 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
One is string similarity based on edit distance .	{ "relations": { "used for": [ { "head": { "text": "edit distance", "start": 34, "end": 47 }, "tail": { "text": "string similarity", "start": 7, "end": 24 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
The other is contextual similarity by a vector space model .	{ "relations": { "used for": [ { "head": { "text": "vector space model", "start": 40, "end": 58 }, "tail": { "text": "contextual similarity", "start": 13, "end": 34 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Experimental results show that the method performed a 0.889 F-measure in an open test .	{ "relations": { "evaluate for": [ { "head": { "text": "F-measure", "start": 60, "end": 69 }, "tail": { "text": "method", "start": 35, "end": 41 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Uncertainty handling plays an important role during shape tracking .	{ "relations": { "used for": [ { "head": { "text": "Uncertainty handling", "start": 0, "end": 20 }, "tail": { "text": "shape tracking", "start": 52, "end": 66 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We have recently shown that the fusion of measurement information with system dynamics and shape priors greatly improves the tracking performance for very noisy images such as ultrasound sequences -LSB- 22 -RSB- .	{ "relations": { "used for": [ { "head": { "text": "fusion of measurement information with system dynamics and shape priors", "start": 32, "end": 103 }, "tail": { "text": "tracking", "start": 125, "end": 133 } ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Nevertheless , this approach required user initialization of the tracking process .	{ "relations": { "used for": [ { "head": { "text": "user initialization", "start": 38, "end": 57 }, "tail": { "text": "approach", "start": 20, "end": 28 } }, { "head": { "text": "user in...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
This paper solves the automatic initial-ization problem by performing boosted shape detection as a generic measurement process and integrating it in our tracking framework .	{ "relations": { "used for": [ { "head": { "text": "boosted shape detection", "start": 70, "end": 93 }, "tail": { "text": "automatic initial-ization problem", "start": 22, "end": 55 } }, { "head":...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
As a result , we treat all sources of information in a unified way and derive the posterior shape model as the shape with the maximum likelihood .	{ "relations": { "used for": [ { "head": { "text": "maximum likelihood", "start": 126, "end": 144 }, "tail": { "text": "posterior shape model", "start": 82, "end": 103 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Our framework is applied for the automatic tracking of endocardium in ultrasound sequences of the human heart .	{ "relations": { "used for": [ { "head": { "text": "framework", "start": 4, "end": 13 }, "tail": { "text": "automatic tracking of endocardium", "start": 33, "end": 66 } } ], "part of": [ { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Reliable detection and robust tracking results are achieved when compared to existing approaches and inter-expert variations .	{ "relations": { "conjunction": [ { "head": { "text": "detection", "start": 9, "end": 18 }, "tail": { "text": "tracking", "start": 30, "end": 38 } }, { "head": { "text": "approaches", ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We present a syntax-based constraint for word alignment , known as the cohesion constraint .	{ "relations": { "used for": [ { "head": { "text": "syntax-based constraint", "start": 13, "end": 36 }, "tail": { "text": "word alignment", "start": 41, "end": 55 } } ], "hyponym of": [ { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
It requires disjoint English phrases to be mapped to non-overlapping intervals in the French sentence .	{ "relations": { "used for": [ { "head": { "text": "English phrases", "start": 21, "end": 36 }, "tail": { "text": "It", "start": 0, "end": 2 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We evaluate the utility of this constraint in two different algorithms .	{ "relations": { "evaluate for": [ { "head": { "text": "algorithms", "start": 60, "end": 70 }, "tail": { "text": "constraint", "start": 32, "end": 42 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
The results show that it can provide a significant improvement in alignment quality .	{ "relations": { "evaluate for": [ { "head": { "text": "alignment quality", "start": 66, "end": 83 }, "tail": { "text": "it", "start": 22, "end": 24 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We present a novel entity-based representation of discourse which is inspired by Centering Theory and can be computed automatically from raw text .	{ "relations": { "used for": [ { "head": { "text": "Centering Theory", "start": 81, "end": 97 }, "tail": { "text": "entity-based representation of discourse", "start": 19, "end": 59 } }, { "head":...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
We view coherence assessment as a ranking learning problem and show that the proposed discourse representation supports the effective learning of a ranking function .	{ "relations": { "used for": [ { "head": { "text": "ranking learning problem", "start": 34, "end": 58 }, "tail": { "text": "coherence assessment", "start": 8, "end": 28 } }, { "head": { ...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
Our experiments demonstrate that the induced model achieves significantly higher accuracy than a state-of-the-art coherence model .	{ "relations": { "compare": [ { "head": { "text": "induced model", "start": 37, "end": 50 }, "tail": { "text": "coherence model", "start": 114, "end": 129 } } ], "evaluate for": [ { "head"...	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
This paper introduces a robust interactive method for speech understanding .	{ "relations": { "used for": [ { "head": { "text": "robust interactive method", "start": 24, "end": 49 }, "tail": { "text": "speech understanding", "start": 54, "end": 74 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
The generalized LR parsing is enhanced in this approach .	{ "relations": { "used for": [ { "head": { "text": "approach", "start": 47, "end": 55 }, "tail": { "text": "generalized LR parsing", "start": 4, "end": 26 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...
When a very noisy portion is detected , the parser skips that portion using a fake non-terminal symbol .	{ "relations": { "used for": [ { "head": { "text": "non-terminal symbol", "start": 83, "end": 102 }, "tail": { "text": "parser", "start": 44, "end": 50 } } ] } }	{ "relations": [ { "label": "used for", "description": "B is used for A, B models A, A is trained on B, B exploits A, A is based on B." }, { "label": "evaluate for", "description": "B evaluates A, B is a metric for A, A is evaluated on/by B." }, { "label": "feature of...

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 .

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In this paper , a novel method to learn the intrinsic object structure for robust visual tracking is proposed .

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The basic assumption is that the parameterized object state lies on a low dimensional manifold and can be learned from training data .

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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 .

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Secondly the dynamical model is derived and trained based on this intrinsic representation .

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Thirdly the learned intrinsic object structure is integrated into a particle-filter style tracker .

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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 .

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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 .

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The proposed method also has the potential to solve other type of tracking problems .

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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 .

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Electrolaryngeal speech is one of the typical types of alaryngeal speech produced by an alternative speaking method for laryngectomees .

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However , the sound quality of NAM and electrolaryngeal speech suffers from lack of naturalness .

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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 .

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An implementation on devices that are highly portable but have limited computational resources would greatly contribute to its practical use .

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In this paper we further implement real-time VC on a DSP .

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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 .

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We conduct experimental evaluations and show that real-time VC is capable of running on a DSP with little degradation .

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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 .

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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 ...

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The paraphrase sets produced by this method thus seem adequate as reference sets to be used for MT evaluation .

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Graph unification remains the most expensive part of unification-based grammar parsing .

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We focus on one speed-up element in the design of unification algorithms : avoidance of copying of unmodified subgraphs .

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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 .

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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 .

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We describe a novel technique and implemented system for constructing a subcategorization dictionary from textual corpora .

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We also demonstrate that a subcategorization dictionary built with the system improves the accuracy of a parser by an appreciable amount

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A number of powerful registration criteria have been developed in the last decade , most prominently the criterion of maximum mutual information .

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Although this criterion provides for good registration results in many applications , it remains a purely low-level criterion .

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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 .

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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 .

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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 .

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We present a method for synthesizing complex , photo-realistic facade images , from a single example .

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After parsing the example image into its semantic components , a tiling for it is generated .

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Novel tilings can then be created , yielding facade textures with different dimensions or with occluded parts inpainted .

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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 .

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Promising results for multiple standard datasets -- in particular for the different building styles they contain -- demonstrate the potential of the method .

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We introduce a new interactive corpus exploration tool called InfoMagnets .

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InfoMagnets aims at making exploratory corpus analysis accessible to researchers who are not experts in text mining .

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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- .

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As an educational tool , it has been used as part of a unit on protocol analysis in an Educational Research Methods course .

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Sources of training data suitable for language modeling of conversational speech are limited .

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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 .

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We present a method for detecting 3D objects using multi-modalities .

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While it is generic , we demonstrate it on the combination of an image and a dense depth map which give complementary object information .

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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 .

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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 .

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Building such a representation requires the capability to register all the frames with respect to the dominant object in the scene , a task which has been , in the past , addressed through temporally localized motion estimates .

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To avoid this oscillation , we augment the motion model with a generic temporal constraint which increases the robustness against competing interpretations , leading to more meaningful content summarization .

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In cross-domain learning , there is a more challenging problem that the domain divergence involves more than one dominant factors , e.g. , different viewpoints , various resolutions and changing illuminations .

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Fortunately , an intermediate domain could often be found to build a bridge across them to facilitate the learning problem .

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In this paper , we propose a Coupled Marginalized Denoising Auto-encoders framework to address the cross-domain problem .

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Specifically , we design two marginalized denoising auto-encoders , one for the target and the other for source as well as the intermediate one .

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To better couple the two denoising auto-encoders learning , we incorporate a feature mapping , which tends to transfer knowledge between the intermediate domain and the target one .

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Furthermore , the maximum margin criterion , e.g. , intra-class com-pactness and inter-class penalty , on the output layer is imposed to seek more discriminative features across different domains .

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Extensive experiments on two tasks have demonstrated the superiority of our method over the state-of-the-art methods .

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Basically , a set of age-group specific dictionaries are learned , where the dictionary bases corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups , and a linear combination of these patterns expresses a particular personalized aging process ...

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First , beyond the aging dictionaries , each subject may have extra personalized facial characteristics , e.g. mole , which are invariant in the aging process .

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Thus a personality-aware coupled reconstruction loss is utilized to learn the dictionaries based on face pairs from neighboring age groups .

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Extensive experiments well demonstrate the advantages of our proposed solution over other state-of-the-arts in term of personalized aging progression , as well as the performance gain for cross-age face verification by synthesizing aging faces .

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We propose a draft scheme of the model formalizing the structure of communicative context in dialogue interaction .

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Visitors who browse the web from wireless PDAs , cell phones , and pagers are frequently stymied by web interfaces optimized for desktop PCs .

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In this paper we develop an algorithm , MINPATH , that automatically improves wireless web navigation by suggesting useful shortcut links in real time .

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MINPATH finds shortcuts by using a learned model of web visitor behavior to estimate the savings of shortcut links , and suggests only the few best links .

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We explore a variety of predictive models , including Na ¨ ıve Bayes mixture models and mixtures of Markov models , and report empirical evidence that MINPATH finds useful shortcuts that save substantial navigational effort .

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This paper describes a particular approach to parsing that utilizes recent advances in unification-based parsing and in classification-based knowledge representation .

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As unification-based grammatical frameworks are extended to handle richer descriptions of linguistic information , they begin to share many of the properties that have been developed in KL-ONE-like knowledge representation systems .

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This commonality suggests that some of the classification-based representation techniques can be applied to unification-based linguistic descriptions .

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This merging supports the integration of semantic and syntactic information into the same system , simultaneously subject to the same types of processes , in an efficient manner .

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The use of a KL-ONE style representation for parsing and semantic interpretation was first explored in the PSI-KLONE system -LSB- 2 -RSB- , in which parsing is characterized as an inference process called incremental description refinement .

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In this paper we discuss a proposed user knowledge modeling architecture for the ICICLE system , a language tutoring application for deaf learners of written English .

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The model will represent the language proficiency of the user and is designed to be referenced during both writing analysis and feedback production .

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We motivate our model design by citing relevant research on second language and cognitive skill acquisition , and briefly discuss preliminary empirical evidence supporting the design .

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We conclude by showing how our design can provide a rich and robust information base to a language assessment / correction application by modeling user proficiency at a high level of granularity and specificity .

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Constraint propagation is one of the key techniques in constraint programming , and a large body of work has built up around it .

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In this paper we present SHORTSTR2 , a development of the Simple Tabular Reduction algorithm STR2 + .

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We show that SHORTSTR2 is complementary to the existing algorithms SHORTGAC and HAGGISGAC that exploit short supports , while being much simpler .

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When a constraint is amenable to short supports , the short support set can be exponentially smaller than the full-length support set .

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We also show that SHORTSTR2 can be combined with a simple algorithm to identify short supports from full-length supports , to provide a superior drop-in replacement for STR2 + .

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We propose a detection method for orthographic variants caused by transliteration in a large corpus .

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The method employs two similarities .

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One is string similarity based on edit distance .

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The other is contextual similarity by a vector space model .

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Experimental results show that the method performed a 0.889 F-measure in an open test .

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Uncertainty handling plays an important role during shape tracking .

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We have recently shown that the fusion of measurement information with system dynamics and shape priors greatly improves the tracking performance for very noisy images such as ultrasound sequences -LSB- 22 -RSB- .

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Nevertheless , this approach required user initialization of the tracking process .

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This paper solves the automatic initial-ization problem by performing boosted shape detection as a generic measurement process and integrating it in our tracking framework .

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As a result , we treat all sources of information in a unified way and derive the posterior shape model as the shape with the maximum likelihood .

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Our framework is applied for the automatic tracking of endocardium in ultrasound sequences of the human heart .

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Reliable detection and robust tracking results are achieved when compared to existing approaches and inter-expert variations .

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We present a syntax-based constraint for word alignment , known as the cohesion constraint .

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It requires disjoint English phrases to be mapped to non-overlapping intervals in the French sentence .

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We evaluate the utility of this constraint in two different algorithms .

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The results show that it can provide a significant improvement in alignment quality .

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We present a novel entity-based representation of discourse which is inspired by Centering Theory and can be computed automatically from raw text .

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We view coherence assessment as a ranking learning problem and show that the proposed discourse representation supports the effective learning of a ranking function .

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Our experiments demonstrate that the induced model achieves significantly higher accuracy than a state-of-the-art coherence model .

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This paper introduces a robust interactive method for speech understanding .

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The generalized LR parsing is enhanced in this approach .

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When a very noisy portion is detected , the parser skips that portion using a fake non-terminal symbol .

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