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 |
| Place of Publication | New Orleans, USA |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 1017–1021 |
| Number of pages | 4 |
| 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 |
Conference
| Conference | 12th International Workshop on Semantic Evaluation (SemEval-2018) |
|---|---|
| Country | United States |
| City | New Orleans |
| Period | 5/06/18 → 6/06/18 |
Cite this
Mao, R., Chen, G., Li, R., & Lin, C. (2018). ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet. In The International Workshop on Semantic Evaluation: Proceedings of the Twelfth Workshop (pp. 1017–1021). Association for Computational Linguistics (ACL). http://www.aclweb.org/anthology/S18-1169