Richard-Bollans, A orcid.org/0000-0003-1980-0107, Gómez Álvarez, L and Cohn, AG orcid.org/0000-0002-7652-8907 (2023) Identifying and modelling polysemous senses of spatial prepositions in referring expressions. Cognitive Systems Research, 77. pp. 45-61. ISSN 1389-0417
Abstract
In this paper we analyse the issue of reference using spatial language and examine how the polysemy exhibited by spatial prepositions can be incorporated into semantic models for situated dialogue. After providing a brief overview of polysemy in spatial language and a review of related work, we describe an experimental study we used to collect data on a set of relevant spatial prepositions. We then establish a semantic model in which to integrate polysemy (the Baseline Prototype Model), which we test against a Simple Relation Model and a Perceptron Model. To incorporate polysemy into the baseline model we introduce two methods of identifying polysemes in grounded settings. The first is based on ‘ideal meanings’ and a modification of the ‘principled polysemy’ framework and the second is based on ‘object-specific features’. In order to compare polysemes and aid typicality judgements we then introduce a notion of ‘polyseme hierarchy’. Finally, we test the performance of the polysemy models against the Baseline Prototype Model and Perceptron Model and discuss the improvements shown by the polysemy models.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) |
Keywords: | Semantics; Spatial language; Polysemy; Referring expressions |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number Alan Turing Institute No ref given |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 Dec 2022 14:45 |
Last Modified: | 19 Dec 2024 09:02 |
Published Version: | https://doi.org/10.1016/j.cogsys.2022.09.004 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.cogsys.2022.09.004 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194132 |