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Integration of World Knowledge for Natural Language Understanding [electronic resource] /by Ekaterina Ovchinnikova.

by Ovchinnikova, Ekaterina [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Atlantis Thinking Machines: 3Publisher: Paris : Atlantis Press, 2012.Description: XVII, 242p. 16 illus., 2 illus. in color. online resource.ISBN: 9789491216534.Subject(s): Computer science | Electronic data processing | Artificial intelligence | Text processing (Computer science | Translators (Computer programs) | Computer Science | Language Translation and Linguistics | Artificial Intelligence (incl. Robotics) | Mathematical Logic and Formal Languages | Document Preparation and Text Processing | Computing MethodologiesDDC classification: 006.35 Online resources: Click here to access online
Contents:
Preliminaries -- Natural Language Understanding and World Knowledge -- Sources of World Knowledge -- Reasoning for Natural Language Understanding -- Knowledge Base Construction -- Ensuring Consistency -- Abductive Reasoning with the Integrative Knowledge Base -- Evaluation -- Conclusion.
In: Springer eBooksSummary: 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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Preliminaries -- Natural Language Understanding and World Knowledge -- Sources of World Knowledge -- Reasoning for Natural Language Understanding -- Knowledge Base Construction -- Ensuring Consistency -- Abductive Reasoning with the Integrative Knowledge Base -- Evaluation -- Conclusion.

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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