Concerns non-linguistic knowledge required to perform computational natural language understanding (NLU). This book shows that inference-based NLU has the potential for practical large scale applications. Series: Atlantis Thinking Machines. Num Pages: 259 pages, 14 black & white illustrations, 2 colour illustrations, 21 black & white tables, biograph. BIC Classification: UGD; UMA; UYA; UYQL. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 18. Weight in Grams: 555. . 2012. 2012th Edition. hardcover. . . . .

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Bibliographic Details

Title: Integration of World Knowledge for Natural Language Understanding
Publisher: Atlantis Press (Zeger Karssen)
Publication Date: 2012
Language: English
ISBN 10: 949121652X
ISBN 13: 9789491216527
Binding: Hardcover
Condition: New

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Synopsis

This book concerns non-linguistic knowledge required to perform computational natural language understanding (NLU). The main objective of the book is to show that inference-based NLU has the potential for practical large scale applications. First, an introduction to research areas relevant for NLU is given. We review approaches to linguistic meaning, explore knowledge resources, describe semantic parsers, and compare two main forms of inference: deduction and abduction. In the main part of the book, we propose an integrative knowledge base combining lexical-semantic, ontological, and distributional knowledge. A particular attention is payed to ensuring its consistency. We then design a reasoning procedure able to make use of the large scale knowledge base. We experiment both with a deduction-based NLU system and with an abductive reasoner. For evaluation, we use three different NLU tasks: recognizing textual entailment, semantic role labeling, and interpretation of noun dependencies.

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First, an introduction to research areas relevant for NLU is given. We review approaches to linguistic meaning, explore knowledge resources, describe semantic parsers, and compare two main forms of inference: deduction and abduction.

In the main part of the book, we propose an integrative knowledge base combining lexical-semantic, ontological, and distributional knowledge. A particular attention is payed to ensuring its consistency. We then design a reasoning procedure able to make use of the large scale knowledge base. We experiment both with a deduction-based NLU system and with an abductive reasoner. For evaluation, we use three different NLU tasks: recognizing textual entailment, semantic role labeling, and interpretation of noun dependencies.

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