APPLIED TEXTUAL ENTAILMENT: A generic framework to capture shallow semantic inference

 
9783639151206: APPLIED TEXTUAL ENTAILMENT: A generic framework to capture shallow semantic inference

This book introduces the applied notion of textual entailment as a generic empirical task that captures major semantic inferences across many applications. Textual Entailment addresses semantic inference as a direct mapping between language expressions and abstracts the common semantic inferences as needed for text based Natural Language Processing applications. The book defines the task and describes the creation of a benchmark dataset for textual entailment along with proposed evaluation measures. It further describes how textual entailment can be approximated and modeled at the lexical level and proposes a lexical reference subtask and a correspondingly derived dataset. The book further proposes a general probabilistic setting that casts the applied notion of textual entailment in probabilistic terms. This proposed setting may provide a unifying framework for modeling uncertain semantic inferences from texts. Finally, the book presents a novel acquisition algorithm to identify lexical entailment relations from a single corpus focusing on the extraction of verb paraphrases.

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Oren Glickman completed his Ph.D in Computer Science at Bar-Ilan University and holds an M.Sc in Computational Linguistics from Carnegie Melon University and a B.Sc in Math and Computer Science from the Hebrew University. Previously he directed research at Shopping.com (eBay) and was a member of the research staff at Digital Equipment Corporation.

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Book Description Book Condition: New. Publisher/Verlag: VDM Verlag Dr. Müller | A generic framework to capture shallow semantic inference | This book introduces the applied notion of textualentailment as a generic empirical task that capturesmajor semantic inferences across many applications.Textual Entailment addresses semantic inference as adirect mapping between language expressions andabstracts the common semantic inferences as neededfor text based Natural Language Processingapplications. The book defines the task and describesthe creation of a benchmark dataset for textualentailment along with proposed evaluation measures.It further describes how textual entailment can beapproximated and modeled at the lexical level andproposes a lexical reference subtask and acorrespondingly derived dataset. The book furtherproposes a general probabilistic setting that caststhe applied notion of textual entailment inprobabilistic terms. This proposed setting mayprovide a unifying framework for modeling uncertainsemantic inferences from texts. Finally, the bookpresents a novel acquisition algorithm to identifylexical entailment relations from a single corpusfocusing on the extraction of verb paraphrases. | Format: Paperback | Language/Sprache: english | 132 pp. Bookseller Inventory # K9783639151206

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Book Description VDM Verlag Mai 2009, 2009. Taschenbuch. Book Condition: Neu. Neuware - This book introduces the applied notion of textual entailment as a generic empirical task that captures major semantic inferences across many applications. Textual Entailment addresses semantic inference as a direct mapping between language expressions and abstracts the common semantic inferences as needed for text based Natural Language Processing applications. The book defines the task and describes the creation of a benchmark dataset for textual entailment along with proposed evaluation measures. It further describes how textual entailment can be approximated and modeled at the lexical level and proposes a lexical reference subtask and a correspondingly derived dataset. The book further proposes a general probabilistic setting that casts the applied notion of textual entailment in probabilistic terms. This proposed setting may provide a unifying framework for modeling uncertain semantic inferences from texts. Finally, the book presents a novel acquisition algorithm to identify lexical entailment relations from a single corpus focusing on the extraction of verb paraphrases. 132 pp. Englisch. Bookseller Inventory # 9783639151206

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Book Description VDM Verlag Mai 2009, 2009. Taschenbuch. Book Condition: Neu. Neuware - This book introduces the applied notion of textual entailment as a generic empirical task that captures major semantic inferences across many applications. Textual Entailment addresses semantic inference as a direct mapping between language expressions and abstracts the common semantic inferences as needed for text based Natural Language Processing applications. The book defines the task and describes the creation of a benchmark dataset for textual entailment along with proposed evaluation measures. It further describes how textual entailment can be approximated and modeled at the lexical level and proposes a lexical reference subtask and a correspondingly derived dataset. The book further proposes a general probabilistic setting that casts the applied notion of textual entailment in probabilistic terms. This proposed setting may provide a unifying framework for modeling uncertain semantic inferences from texts. Finally, the book presents a novel acquisition algorithm to identify lexical entailment relations from a single corpus focusing on the extraction of verb paraphrases. 132 pp. Englisch. Bookseller Inventory # 9783639151206

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Book Description VDM Verlag Mai 2009, 2009. Taschenbuch. Book Condition: Neu. This item is printed on demand - Print on Demand Neuware - This book introduces the applied notion of textual 132 pp. Englisch. Bookseller Inventory # 9783639151206

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Book Description VDM Verlag, 2009. Paperback. Book Condition: New. Language: English . Brand New Book. This book introduces the applied notion of textual entailment as a generic empirical task that captures major semantic inferences across many applications. Textual Entailment addresses semantic inference as a direct mapping between language expressions and abstracts the common semantic inferences as needed for text based Natural Language Processing applications. The book defines the task and describes the creation of a benchmark dataset for textual entailment along with proposed evaluation measures. It further describes how textual entailment can be approximated and modeled at the lexical level and proposes a lexical reference subtask and a correspondingly derived dataset. The book further proposes a general probabilistic setting that casts the applied notion of textual entailment in probabilistic terms. This proposed setting may provide a unifying framework for modeling uncertain semantic inferences from texts. Finally, the book presents a novel acquisition algorithm to identify lexical entailment relations from a single corpus focusing on the extraction of verb paraphrases. Bookseller Inventory # KNV9783639151206

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