The Experts below are selected from a list of 19272 Experts worldwide ranked by ideXlab platform
Jacques Chauché - One of the best experts on this subject based on the ideXlab platform.
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How to combine text-mining methods to validate induced verb-object Relations?
Computer Science and Information Systems, 2014Co-Authors: Nicolas Béchet, Jacques Chauché, Violaine Prince, Mathieu RocheAbstract:This paper describes methods using Natural Language Processing approaches to extract and validate induced Syntactic Relations (here restricted to the Verb-Object Relation). These methods use a Syntactic parser and a semantic closeness measure to extract such Relations. Then, their validation is based on two different techniques: A Web Validation system on one part, then a Semantic-Vectorbased approach, and finally different combinations of both techniques in order to rank induced Verb-Object Relations. The Semantic Vector approach is a Roget-based method which computes a Syntactic Relation as a vector. Web Validation uses a search engine to determine the relevance of a Syntactic Relation according to its popularity. An experimental protocol is set up to judge automatically the relevance of the sorted induced Relations. We finally apply our approach on a French corpus of news by using ROC Curves to evaluate the results.
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A Hybrid Approach to Validate Induced Syntactic Relations
2009 International Conference on Advanced Information Networking and Applications Workshops, 2009Co-Authors: Nicolas Béchet, Mathieu Roche, Jacques ChauchéAbstract:We propose in this paper to use NLP approaches to extract and validate induced Syntactic Relations (verb-object). We employ Syntactic parser and a semantic proximity measure to extract them. Then, we focus on a Web validation system, a semantic-vector-based approach, and finally we propose approaches to combine both in order to rank induced Syntactic Relations. The semantic vectors approach is a Roget-based method which computes a Syntactic Relation as a vector. Web validation uses a search engine to determine the relevance of a Syntactic Relation. The systems combine Web validation approach and semantic vectors technique. We apply our approaches on corpus of news, using ROC curves to evaluate the results.
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Towards the Selection of Induced Syntactic Relations
2009Co-Authors: Nicolas Béchet, Mathieu Roche, Jacques ChauchéAbstract:We propose in this paper to use NLP approaches to validate induced syn- tactic Relations. We focus on a Web Validation system, a Semantic Vector-based approach, and finally a Combined system. The Semantic Vector approach is a Roget-based approach which computes a Syntactic Relation as a vector. The Web Validation technique uses a search engine to determine the relevance of a Syntactic Relation. We,experiment our approaches on real-world data set. The ROC curves are used to evaluate the results.
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ECIR - Towards the Selection of Induced Syntactic Relations
Lecture Notes in Computer Science, 2009Co-Authors: Nicolas Béchet, Mathieu Roche, Jacques ChauchéAbstract:We propose in this paper to use NLP approaches to validate induced Syntactic Relations. We focus on a Web Validation system, a Semantic Vector-based approach, and finally a Combined system. The Semantic Vector approach is a Roget-based approach which computes a Syntactic Relation as a vector. The Web Validation technique uses a search engine to determine the relevance of a Syntactic Relation. We experiment our approaches on real-world data set. The ROC curves are used to evaluate the results.
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AINA Workshops - A Hybrid Approach to Validate Induced Syntactic Relations
2009 International Conference on Advanced Information Networking and Applications Workshops, 2009Co-Authors: Nicolas Béchet, Mathieu Roche, Jacques ChauchéAbstract:We propose in this paper to use NLP approaches to extract and validate induced Syntactic Relations (Verb-Object). We employ Syntactic parser and a semantic proximity measure to extract them. Then, we focus on a Web Validation system, a Semantic-Vector-based approach, and finally we propose approaches to combine both in order to rank induced Syntactic Relations. The Semantic Vectors approach is a Roget-based method which computes a Syntactic Relation as a vector. Web Validation uses a search engine to determine the relevance of a Syntactic Relation. The systems combine Web Validation approach and Semantic Vectors technique. We apply our approaches on corpus of news, using ROC curves to evaluate the results.
Nicolas Béchet - One of the best experts on this subject based on the ideXlab platform.
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How to combine text-mining methods to validate induced verb-object Relations?
Computer Science and Information Systems, 2014Co-Authors: Nicolas Béchet, Jacques Chauché, Violaine Prince, Mathieu RocheAbstract:This paper describes methods using Natural Language Processing approaches to extract and validate induced Syntactic Relations (here restricted to the Verb-Object Relation). These methods use a Syntactic parser and a semantic closeness measure to extract such Relations. Then, their validation is based on two different techniques: A Web Validation system on one part, then a Semantic-Vectorbased approach, and finally different combinations of both techniques in order to rank induced Verb-Object Relations. The Semantic Vector approach is a Roget-based method which computes a Syntactic Relation as a vector. Web Validation uses a search engine to determine the relevance of a Syntactic Relation according to its popularity. An experimental protocol is set up to judge automatically the relevance of the sorted induced Relations. We finally apply our approach on a French corpus of news by using ROC Curves to evaluate the results.
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A Hybrid Approach to Validate Induced Syntactic Relations
2009 International Conference on Advanced Information Networking and Applications Workshops, 2009Co-Authors: Nicolas Béchet, Mathieu Roche, Jacques ChauchéAbstract:We propose in this paper to use NLP approaches to extract and validate induced Syntactic Relations (verb-object). We employ Syntactic parser and a semantic proximity measure to extract them. Then, we focus on a Web validation system, a semantic-vector-based approach, and finally we propose approaches to combine both in order to rank induced Syntactic Relations. The semantic vectors approach is a Roget-based method which computes a Syntactic Relation as a vector. Web validation uses a search engine to determine the relevance of a Syntactic Relation. The systems combine Web validation approach and semantic vectors technique. We apply our approaches on corpus of news, using ROC curves to evaluate the results.
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Towards the Selection of Induced Syntactic Relations
2009Co-Authors: Nicolas Béchet, Mathieu Roche, Jacques ChauchéAbstract:We propose in this paper to use NLP approaches to validate induced syn- tactic Relations. We focus on a Web Validation system, a Semantic Vector-based approach, and finally a Combined system. The Semantic Vector approach is a Roget-based approach which computes a Syntactic Relation as a vector. The Web Validation technique uses a search engine to determine the relevance of a Syntactic Relation. We,experiment our approaches on real-world data set. The ROC curves are used to evaluate the results.
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ECIR - Towards the Selection of Induced Syntactic Relations
Lecture Notes in Computer Science, 2009Co-Authors: Nicolas Béchet, Mathieu Roche, Jacques ChauchéAbstract:We propose in this paper to use NLP approaches to validate induced Syntactic Relations. We focus on a Web Validation system, a Semantic Vector-based approach, and finally a Combined system. The Semantic Vector approach is a Roget-based approach which computes a Syntactic Relation as a vector. The Web Validation technique uses a search engine to determine the relevance of a Syntactic Relation. We experiment our approaches on real-world data set. The ROC curves are used to evaluate the results.
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AINA Workshops - A Hybrid Approach to Validate Induced Syntactic Relations
2009 International Conference on Advanced Information Networking and Applications Workshops, 2009Co-Authors: Nicolas Béchet, Mathieu Roche, Jacques ChauchéAbstract:We propose in this paper to use NLP approaches to extract and validate induced Syntactic Relations (Verb-Object). We employ Syntactic parser and a semantic proximity measure to extract them. Then, we focus on a Web Validation system, a Semantic-Vector-based approach, and finally we propose approaches to combine both in order to rank induced Syntactic Relations. The Semantic Vectors approach is a Roget-based method which computes a Syntactic Relation as a vector. Web Validation uses a search engine to determine the relevance of a Syntactic Relation. The systems combine Web Validation approach and Semantic Vectors technique. We apply our approaches on corpus of news, using ROC curves to evaluate the results.
Mathieu Roche - One of the best experts on this subject based on the ideXlab platform.
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How to combine text-mining methods to validate induced verb-object Relations?
Computer Science and Information Systems, 2014Co-Authors: Nicolas Béchet, Jacques Chauché, Violaine Prince, Mathieu RocheAbstract:This paper describes methods using Natural Language Processing approaches to extract and validate induced Syntactic Relations (here restricted to the Verb-Object Relation). These methods use a Syntactic parser and a semantic closeness measure to extract such Relations. Then, their validation is based on two different techniques: A Web Validation system on one part, then a Semantic-Vectorbased approach, and finally different combinations of both techniques in order to rank induced Verb-Object Relations. The Semantic Vector approach is a Roget-based method which computes a Syntactic Relation as a vector. Web Validation uses a search engine to determine the relevance of a Syntactic Relation according to its popularity. An experimental protocol is set up to judge automatically the relevance of the sorted induced Relations. We finally apply our approach on a French corpus of news by using ROC Curves to evaluate the results.
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A Hybrid Approach to Validate Induced Syntactic Relations
2009 International Conference on Advanced Information Networking and Applications Workshops, 2009Co-Authors: Nicolas Béchet, Mathieu Roche, Jacques ChauchéAbstract:We propose in this paper to use NLP approaches to extract and validate induced Syntactic Relations (verb-object). We employ Syntactic parser and a semantic proximity measure to extract them. Then, we focus on a Web validation system, a semantic-vector-based approach, and finally we propose approaches to combine both in order to rank induced Syntactic Relations. The semantic vectors approach is a Roget-based method which computes a Syntactic Relation as a vector. Web validation uses a search engine to determine the relevance of a Syntactic Relation. The systems combine Web validation approach and semantic vectors technique. We apply our approaches on corpus of news, using ROC curves to evaluate the results.
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Towards the Selection of Induced Syntactic Relations
2009Co-Authors: Nicolas Béchet, Mathieu Roche, Jacques ChauchéAbstract:We propose in this paper to use NLP approaches to validate induced syn- tactic Relations. We focus on a Web Validation system, a Semantic Vector-based approach, and finally a Combined system. The Semantic Vector approach is a Roget-based approach which computes a Syntactic Relation as a vector. The Web Validation technique uses a search engine to determine the relevance of a Syntactic Relation. We,experiment our approaches on real-world data set. The ROC curves are used to evaluate the results.
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ECIR - Towards the Selection of Induced Syntactic Relations
Lecture Notes in Computer Science, 2009Co-Authors: Nicolas Béchet, Mathieu Roche, Jacques ChauchéAbstract:We propose in this paper to use NLP approaches to validate induced Syntactic Relations. We focus on a Web Validation system, a Semantic Vector-based approach, and finally a Combined system. The Semantic Vector approach is a Roget-based approach which computes a Syntactic Relation as a vector. The Web Validation technique uses a search engine to determine the relevance of a Syntactic Relation. We experiment our approaches on real-world data set. The ROC curves are used to evaluate the results.
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AINA Workshops - A Hybrid Approach to Validate Induced Syntactic Relations
2009 International Conference on Advanced Information Networking and Applications Workshops, 2009Co-Authors: Nicolas Béchet, Mathieu Roche, Jacques ChauchéAbstract:We propose in this paper to use NLP approaches to extract and validate induced Syntactic Relations (Verb-Object). We employ Syntactic parser and a semantic proximity measure to extract them. Then, we focus on a Web Validation system, a Semantic-Vector-based approach, and finally we propose approaches to combine both in order to rank induced Syntactic Relations. The Semantic Vectors approach is a Roget-based method which computes a Syntactic Relation as a vector. Web Validation uses a search engine to determine the relevance of a Syntactic Relation. The systems combine Web Validation approach and Semantic Vectors technique. We apply our approaches on corpus of news, using ROC curves to evaluate the results.
Dietrich Klakow - One of the best experts on this subject based on the ideXlab platform.
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ACL - Exploring CorRelation of Dependency Relation Paths for Answer Extraction
Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06, 2006Co-Authors: Dan Shen, Dietrich KlakowAbstract:In this paper, we explore corRelation of dependency Relation paths to rank candidate answers in answer extraction. Using the corRelation measure, we compare dependency Relations of a candidate answer and mapped question phrases in sentence with the corresponding Relations in question. Different from previous studies, we propose an approximate phrase mapping algorithm and incorporate the mapping score into the corRelation measure. The corRelations are further incorporated into a Maximum Entropy-based ranking model which estimates path weights from training. Experimental results show that our method significantly outperforms state-of-the-art Syntactic Relation-based methods by up to 20% in MRR.
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exploring Syntactic Relation patterns for question answering
International Joint Conference on Natural Language Processing, 2005Co-Authors: Dan Shen, Geertjan M Kruijff, Dietrich KlakowAbstract:In this paper, we explore the Syntactic Relation patterns for open-domain factoid question answering. We propose a pattern extraction method to extract the various Relations between the proper answers and different types of question words, including target words, head words, subject words and verbs, from Syntactic trees. We further propose a QA-specific tree kernel to partially match the Syntactic Relation patterns. It makes the more tolerant matching between two patterns and helps to solve the data sparseness problem. Lastly, we incorporate the patterns into a Maximum Entropy Model to rank the answer candidates. The experiment on TREC questions shows that the Syntactic Relation patterns help to improve the performance by 6.91 MRR based on the common features.
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IJCNLP - Exploring Syntactic Relation patterns for question answering
Lecture Notes in Computer Science, 2005Co-Authors: Dan Shen, Geertjan M Kruijff, Dietrich KlakowAbstract:In this paper, we explore the Syntactic Relation patterns for open-domain factoid question answering. We propose a pattern extraction method to extract the various Relations between the proper answers and different types of question words, including target words, head words, subject words and verbs, from Syntactic trees. We further propose a QA-specific tree kernel to partially match the Syntactic Relation patterns. It makes the more tolerant matching between two patterns and helps to solve the data sparseness problem. Lastly, we incorporate the patterns into a Maximum Entropy Model to rank the answer candidates. The experiment on TREC questions shows that the Syntactic Relation patterns help to improve the performance by 6.91 MRR based on the common features.
Dan Shen - One of the best experts on this subject based on the ideXlab platform.
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ACL - Exploring CorRelation of Dependency Relation Paths for Answer Extraction
Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06, 2006Co-Authors: Dan Shen, Dietrich KlakowAbstract:In this paper, we explore corRelation of dependency Relation paths to rank candidate answers in answer extraction. Using the corRelation measure, we compare dependency Relations of a candidate answer and mapped question phrases in sentence with the corresponding Relations in question. Different from previous studies, we propose an approximate phrase mapping algorithm and incorporate the mapping score into the corRelation measure. The corRelations are further incorporated into a Maximum Entropy-based ranking model which estimates path weights from training. Experimental results show that our method significantly outperforms state-of-the-art Syntactic Relation-based methods by up to 20% in MRR.
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exploring Syntactic Relation patterns for question answering
International Joint Conference on Natural Language Processing, 2005Co-Authors: Dan Shen, Geertjan M Kruijff, Dietrich KlakowAbstract:In this paper, we explore the Syntactic Relation patterns for open-domain factoid question answering. We propose a pattern extraction method to extract the various Relations between the proper answers and different types of question words, including target words, head words, subject words and verbs, from Syntactic trees. We further propose a QA-specific tree kernel to partially match the Syntactic Relation patterns. It makes the more tolerant matching between two patterns and helps to solve the data sparseness problem. Lastly, we incorporate the patterns into a Maximum Entropy Model to rank the answer candidates. The experiment on TREC questions shows that the Syntactic Relation patterns help to improve the performance by 6.91 MRR based on the common features.
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IJCNLP - Exploring Syntactic Relation patterns for question answering
Lecture Notes in Computer Science, 2005Co-Authors: Dan Shen, Geertjan M Kruijff, Dietrich KlakowAbstract:In this paper, we explore the Syntactic Relation patterns for open-domain factoid question answering. We propose a pattern extraction method to extract the various Relations between the proper answers and different types of question words, including target words, head words, subject words and verbs, from Syntactic trees. We further propose a QA-specific tree kernel to partially match the Syntactic Relation patterns. It makes the more tolerant matching between two patterns and helps to solve the data sparseness problem. Lastly, we incorporate the patterns into a Maximum Entropy Model to rank the answer candidates. The experiment on TREC questions shows that the Syntactic Relation patterns help to improve the performance by 6.91 MRR based on the common features.
