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.
Original language | English |
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Title of host publication | The International Workshop on Semantic Evaluation |
Subtitle of host publication | Proceedings of the Twelfth Workshop |
Editors | Marianna Apidianaki, Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat |
Place of Publication | New Orleans, USA |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 1017–1021 |
Number of pages | 4 |
ISBN (Electronic) | 9781948087209 |
ISBN (Print) | 978-1-948087-20-9 |
Publication status | Published - 1 Jun 2018 |
Event | 12th International Workshop on Semantic Evaluation (SemEval-2018) - New Orleans, United States Duration: 5 Jun 2018 → 6 Jun 2018 |
Publication series
Name | NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop |
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Conference
Conference | 12th International Workshop on Semantic Evaluation (SemEval-2018) |
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Country/Territory | United States |
City | New Orleans |
Period | 5/06/18 → 6/06/18 |
Bibliographical note
Funding Information:This work is supported by the award made by the UK Engineering and Physical Sciences Research Council (Grant number: EP/P005810/1).
Publisher Copyright:
© 2018 Association for Computational Linguistics