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Analysis of word co-occurrence in human literature for supporting semantic correspondence discovery

Authors:

Jorge Martinez-Gil,

Mario PichlerAuthors Info & Claims

i-KNOW '14: Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business

Article No.: 1, Pages 1 - 7

https://doi.org/10.1145/2637748.2638422

Published: 16 September 2014 Publication History

Abstract

Semantic similarity measurement aims to determine the likeness between two text expressions that use different lexicographies for representing the same real object or idea. In this work, we describe the way to exploit broad cultural trends for identifying semantic similarity. This is possible through the quantitative analysis of a vast digital book collection representing the digested history of humanity. Our research work has revealed that appropriately analyzing the co-occurrence of words in some periods of human literature can help us to determine the semantic similarity between these words by means of computers with a high degree of accuracy.

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Cited By

Amur ZHooi YSodhar IBhanbhro HDahri K(2022)State-of-the Art: Short Text Semantic Similarity (STSS) Techniques in Question Answering Systems (QAS)International Conference on Artificial Intelligence for Smart Community10.1007/978-981-16-2183-3_98(1033-1044)Online publication date: 14-Nov-2022
https://doi.org/10.1007/978-981-16-2183-3_98

Index Terms

Analysis of word co-occurrence in human literature for supporting semantic correspondence discovery
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information systems applications

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cover image ACM Other conferences

i-KNOW '14: Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business

September 2014

262 pages

ISBN:9781450327695

DOI:10.1145/2637748

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 September 2014

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European Fund for Regional Development
State of Upper Austria
Österreichische Forschungsförderungsgesellschaft

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i-KNOW '14

i-KNOW '14: 14th International Conference on Knowledge Technologies and Data-driven Business

September 16 - 19, 2014

Graz, Austria

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i-KNOW '14 Paper Acceptance Rate 25 of 73 submissions, 34%;

Overall Acceptance Rate 77 of 238 submissions, 32%

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Cited By

Amur ZHooi YSodhar IBhanbhro HDahri K(2022)State-of-the Art: Short Text Semantic Similarity (STSS) Techniques in Question Answering Systems (QAS)International Conference on Artificial Intelligence for Smart Community10.1007/978-981-16-2183-3_98(1033-1044)Online publication date: 14-Nov-2022
https://doi.org/10.1007/978-981-16-2183-3_98

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