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Composition of semantic relations: Theoretical framework and case study

Composition of semantic relations: Theoretical framework and case study Composition of Semantic Relations: Theoretical Framework and Case Study EDUARDO BLANCO and DAN MOLDOVAN, The University of Texas at Dallas Extracting semantic relations from text is a preliminary step towards understanding the meaning of text. The more semantic relations are extracted from a sentence, the better the representation of the knowledge encoded into that sentence. This article introduces a framework for the Composition of Semantic Relations (CSR). CSR aims to reveal more text semantics than existing semantic parsers by composing new relations out of previously extracted relations. Semantic relations are defined using vectors of semantic primitives, and an algebra is suggested to manipulate these vectors according to a CSR algorithm. Inference axioms that combine two relations and yield another relation are generated automatically. CSR is a language-agnostic, inventory-independent method to extract semantic relations. The formalism has been applied to a set of 26 well-known relations and results are reported. Categories and Subject Descriptors: I.2.7 [Artificial Intelligence]: Natural Language Processing--Language parsing and understanding General Terms: Algorithms, Design, Theory, Experimentation Additional Key Words and Phrases: Semantic relations, relation extraction, relation inference ACM Reference Format: Blanco, E. and Moldovan, D. 2013. Composition of semantic relations: Theoretical framework and case study. ACM http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Speech and Language Processing (TSLP) Association for Computing Machinery

Composition of semantic relations: Theoretical framework and case study

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2013 by ACM Inc.
ISSN
1550-4875
DOI
10.1145/2513146
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Abstract

Composition of Semantic Relations: Theoretical Framework and Case Study EDUARDO BLANCO and DAN MOLDOVAN, The University of Texas at Dallas Extracting semantic relations from text is a preliminary step towards understanding the meaning of text. The more semantic relations are extracted from a sentence, the better the representation of the knowledge encoded into that sentence. This article introduces a framework for the Composition of Semantic Relations (CSR). CSR aims to reveal more text semantics than existing semantic parsers by composing new relations out of previously extracted relations. Semantic relations are defined using vectors of semantic primitives, and an algebra is suggested to manipulate these vectors according to a CSR algorithm. Inference axioms that combine two relations and yield another relation are generated automatically. CSR is a language-agnostic, inventory-independent method to extract semantic relations. The formalism has been applied to a set of 26 well-known relations and results are reported. Categories and Subject Descriptors: I.2.7 [Artificial Intelligence]: Natural Language Processing--Language parsing and understanding General Terms: Algorithms, Design, Theory, Experimentation Additional Key Words and Phrases: Semantic relations, relation extraction, relation inference ACM Reference Format: Blanco, E. and Moldovan, D. 2013. Composition of semantic relations: Theoretical framework and case study. ACM

Journal

ACM Transactions on Speech and Language Processing (TSLP) – Association for Computing Machinery

Published: Dec 1, 2013

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