Mao, R., Chen, G., Li, R. et al. (1 more author) (2018) ABDN at SemEval-2018 Task 10 : recognising discriminative attributes using context embeddings and WordNet. In: Apidianaki, M., Mohammad, S.M., May, J., Shutova, E., Carpuat, M. and Bethard, S., (eds.) Proceedings of the 12th International Workshop on Semantic Evaluation. 12th International Workshop on Semantic Evaluation - SemEval-2018, 05-06 Jun 2018, New Orleans, LA, USA. Association for Computational Linguistics (ACL) , pp. 1017-1021. ISBN 9781948087209
Abstract
This paper describes the system that we submitted for SemEval-2018 task 10: capturing discriminative attributes. Our system is built upon a simple idea of measuring the attribute word’s similarity with each of the two semantically similar words, based on an extended word embedding method and WordNet. Instead of computing the similarities between the attribute and semantically similar words by using standard word embeddings, we propose a novel method that combines word and context embeddings which can better measure similarities. Our model is simple and effective, which achieves an average F1 score of 0.62 on the test set.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Association for Computational Linguistics. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Jun 2020 11:14 |
Last Modified: | 11 Jun 2020 11:14 |
Published Version: | https://www.aclweb.org/anthology/S18-1169 |
Status: | Published |
Publisher: | Association for Computational Linguistics (ACL) |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161489 |