11
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Toward an Integrative Approach for Making Sense Distinctions

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Word senses are the fundamental unit of description in lexicography, yet it is rarely the case that different dictionaries reach any agreement on the number and definition of senses in a language. With the recent rise in natural language processing and other computational approaches there is an increasing demand for quantitatively validated sense catalogues of words, yet no consensus methodology exists. In this paper, we look at four main approaches to making sense distinctions: formal, cognitive, distributional, and intercultural and examine the strengths and weaknesses of each approach. We then consider how these may be combined into a single sound methodology. We illustrate this by examining two English words, “wing” and “fish,” using existing resources for each of these four approaches and illustrate the weaknesses of each. We then look at the impact of such an integrated method and provide some future perspectives on the research that is necessary to reach a principled method for making sense distinctions.

          Related collections

          Most cited references83

          • Record: found
          • Abstract: found
          • Article: not found

          Community structure in social and biological networks.

          A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known--a collaboration network and a food web--and find that it detects significant and informative community divisions in both cases.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Community detection in graphs

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              WordNet: a lexical database for English

                Bookmark

                Author and article information

                Contributors
                Journal
                Front Artif Intell
                Front Artif Intell
                Front. Artif. Intell.
                Frontiers in Artificial Intelligence
                Frontiers Media S.A.
                2624-8212
                07 February 2022
                2022
                : 5
                : 745626
                Affiliations
                Data Science Institute, National University of Ireland Galway , Galway, Ireland
                Author notes

                Edited by: Simon De Deyne, The University of Melbourne, Australia

                Reviewed by: Adam Pease, Articulate Software, United States; Blair Armstrong, University of Toronto Scarborough, Canada

                *Correspondence: John P. McCrae john@ 123456mccr.ae

                This article was submitted to Language and Computation, a section of the journal Frontiers in Artificial Intelligence

                Article
                10.3389/frai.2022.745626
                8859323
                ac867664-2b67-4983-a38f-b6ca78ca6069
                Copyright © 2022 McCrae, Fransen, Ahmadi, Buitelaar and Goswami.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 22 July 2021
                : 11 January 2022
                Page count
                Figures: 7, Tables: 5, Equations: 0, References: 85, Pages: 18, Words: 15309
                Funding
                Funded by: Enterprise Ireland, doi 10.13039/501100001588;
                Funded by: Science Foundation Ireland, doi 10.13039/501100001602;
                Funded by: Irish Research Council, doi 10.13039/501100002081;
                Funded by: Horizon 2020 Framework Programme, doi 10.13039/100010661;
                Categories
                Artificial Intelligence
                Original Research

                lexicography,word senses,semantics,distributional semantics,cognitive semantics,multilinguality,generative lexicon,wordnets

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content31

                Most referenced authors864