University of Oulu

Oussalah, M., Mohamed, M. Knowledge-based sentence semantic similarity: algebraical properties. Prog Artif Intell (2021). https://doi.org/10.1007/s13748-021-00248-0

Knowledge-based sentence semantic similarity : algebraical properties

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Author: Oussalah, Mourad1; Mohamed, Muhidin2
Organizations: 1Faculty of Information Technology and Electrical Engineering, CMVS, University of Oulu, 90014 Oulu, Finland
2Operations and Information Management Department, Aston University, Birmingham, UK
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021100449270
Language: English
Published: Springer Nature, 2021
Publish Date: 2021-10-04
Description:

Abstract

Determining the extent to which two text snippets are semantically equivalent is a well-researched topic in the areas of natural language processing, information retrieval and text summarization. The sentence-to-sentence similarity scoring is extensively used in both generic and query-based summarization of documents as a significance or a similarity indicator. Nevertheless, most of these applications utilize the concept of semantic similarity measure only as a tool, without paying importance to the inherent properties of such tools that ultimately restrict the scope and technical soundness of the underlined applications. This paper aims to contribute to fill in this gap. It investigates three popular WordNet hierarchical semantic similarity measures, namely path-length, Wu and Palmer and Leacock and Chodorow, from both algebraical and intuitive properties, highlighting their inherent limitations and theoretical constraints. We have especially examined properties related to range and scope of the semantic similarity score, incremental monotonicity evolution, monotonicity with respect to hyponymy/hypernymy relationship as well as a set of interactive properties. Extension from word semantic similarity to sentence similarity has also been investigated using a pairwise canonical extension. Properties of the underlined sentence-to-sentence similarity are examined and scrutinized. Next, to overcome inherent limitations of WordNet semantic similarity in terms of accounting for various Part-of-Speech word categories, a WordNet “All word-To-Noun conversion” that makes use of Categorial Variation Database (CatVar) is put forward and evaluated using a publicly available dataset with a comparison with some state-of-the-art methods. The finding demonstrates the feasibility of the proposal and opens up new opportunities in information retrieval and natural language processing tasks.

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Series: Progress in artificial intelligence
ISSN: 2192-6352
ISSN-E: 2192-6360
ISSN-L: 2192-6352
Volume: Online First
Issue: Online First
Pages: 1 - 21
DOI: 10.1007/s13748-021-00248-0
OADOI: https://oadoi.org/10.1007/s13748-021-00248-0
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
Funding: This work is partly supported by EU YoungRes project (#823701), which is gratefully acknowledged.
Copyright information: © The Authors 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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