ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet

Rui Mao, Guanyi Chen, Ruizhe Li, Chenghua Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 languageEnglish
Title of host publicationThe International Workshop on Semantic Evaluation
Subtitle of host publicationProceedings of the Twelfth Workshop
Place of PublicationNew Orleans, USA
PublisherAssociation for Computational Linguistics (ACL)
Number of pages4
ISBN (Print)978-1-948087-20-9
Publication statusPublished - 1 Jun 2018
Event12th International Workshop on Semantic Evaluation (SemEval-2018) - New Orleans, United States
Duration: 5 Jun 20186 Jun 2018


Conference12th International Workshop on Semantic Evaluation (SemEval-2018)
CountryUnited States
CityNew Orleans

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