research-article

Composition of semantic relations: Theoretical framework and case study

Authors:

Eduardo Blanco,

Dan MoldovanAuthors Info & Claims

ACM Transactions on Speech and Language Processing (TSLP), Volume 10, Issue 4

Article No.: 17, Pages 1 - 36

https://doi.org/10.1145/2513146

Published: 03 January 2014 Publication History

Abstract

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.

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Cited By

Erekhinskaya TBalakrishna MTatu MWerner SMoldovan DAdam NCassel BYesha YFuruta RWeigle M(2016)Knowledge Extraction for Literature ReviewProceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries10.1145/2910896.2925441(221-222)Online publication date: 19-Jun-2016
https://dl.acm.org/doi/10.1145/2910896.2925441
Cai HLiu LChang X(2016)Research on semantic relation acquisition and automatic synthesis based on Wikipedia2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)10.1109/FSKD.2016.7603409(1564-1571)Online publication date: Aug-2016
https://doi.org/10.1109/FSKD.2016.7603409
Liu LXu ZCai HDiao LYan S(2014)Acquiring semantic relation pattern from large microblog text2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)10.1109/FSKD.2014.6980908(633-637)Online publication date: Aug-2014
https://doi.org/10.1109/FSKD.2014.6980908

Index Terms

Composition of semantic relations: Theoretical framework and case study
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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cover image ACM Transactions on Speech and Language Processing

ACM Transactions on Speech and Language Processing Volume 10, Issue 4

December 2013

206 pages

ISSN:1550-4875

EISSN:1550-4883

DOI:10.1145/2560566

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Copyright © 2014 ACM.

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Publication History

Published: 03 January 2014

Accepted: 01 May 2013

Revised: 01 September 2012

Received: 01 November 2011

Published in TSLP Volume 10, Issue 4

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Cited By

Erekhinskaya TBalakrishna MTatu MWerner SMoldovan DAdam NCassel BYesha YFuruta RWeigle M(2016)Knowledge Extraction for Literature ReviewProceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries10.1145/2910896.2925441(221-222)Online publication date: 19-Jun-2016
https://dl.acm.org/doi/10.1145/2910896.2925441
Cai HLiu LChang X(2016)Research on semantic relation acquisition and automatic synthesis based on Wikipedia2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)10.1109/FSKD.2016.7603409(1564-1571)Online publication date: Aug-2016
https://doi.org/10.1109/FSKD.2016.7603409
Liu LXu ZCai HDiao LYan S(2014)Acquiring semantic relation pattern from large microblog text2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)10.1109/FSKD.2014.6980908(633-637)Online publication date: Aug-2014
https://doi.org/10.1109/FSKD.2014.6980908

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