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A graph-based approach to commonsense concept extraction and semantic similarity detection

Published: 13 May 2013 Publication History

Abstract

Commonsense knowledge representation and reasoning support a wide variety of potential applications in fields such as document auto-categorization, Web search enhancement, topic gisting, social process modeling, and concept-level opinion and sentiment analysis. Solutions to these problems, however, demand robust knowledge bases capable of supporting flexible, nuanced reasoning. Populating such knowledge bases is highly time-consuming, making it necessary to develop techniques for deconstructing natural language texts into commonsense concepts. In this work, we propose an approach for effective multi-word commonsense expression extraction from unrestricted English text, in addition to a semantic similarity detection technique allowing additional matches to be found for specific concepts not already present in knowledge bases.

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cover image ACM Other conferences
WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web
May 2013
1636 pages
ISBN:9781450320382
DOI:10.1145/2487788

Sponsors

  • NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
  • CGIBR: Comite Gestor da Internet no Brazil

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

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Author Tags

  1. ai
  2. commonsense knowledge representation and reasoning
  3. natural language processing
  4. semantic similarity

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  • Research-article

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WWW '13
Sponsor:
  • NICBR
  • CGIBR
WWW '13: 22nd International World Wide Web Conference
May 13 - 17, 2013
Rio de Janeiro, Brazil

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WWW '13 Companion Paper Acceptance Rate 831 of 1,250 submissions, 66%;
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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  • (2024)Acquiring and Modeling Abstract Commonsense Knowledge via ConceptualizationArtificial Intelligence10.1016/j.artint.2024.104149(104149)Online publication date: May-2024
  • (2024)Exploring New Horizons in Word Sense Disambiguation and Topic Modeling: Potential of Deep Learning Based Transformers ModelsDigital Humanities Looking at the World10.1007/978-3-031-48941-9_26(341-356)Online publication date: 20-Apr-2024
  • (2024)Substructure Discovery in Commonsense Relations Using Graph Representation LearningIntelligent Systems and Applications10.1007/978-3-031-47721-8_48(714-734)Online publication date: 10-Jan-2024
  • (2023)An experimental study measuring the generalization of fine‐tuned language representation models across commonsense reasoning benchmarksExpert Systems10.1111/exsy.1324340:5Online publication date: 10-Feb-2023
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