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 proceedingPublished conference contribution

2 Citations (Scopus)

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 languageEnglish
Title of host publicationThe International Workshop on Semantic Evaluation
Subtitle of host publicationProceedings of the Twelfth Workshop
EditorsMarianna Apidianaki, Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Place of PublicationNew Orleans, USA
PublisherAssociation for Computational Linguistics (ACL)
Pages1017–1021
Number of pages4
ISBN (Electronic)9781948087209
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

Publication series

NameNAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop

Conference

Conference12th International Workshop on Semantic Evaluation (SemEval-2018)
Country/TerritoryUnited States
CityNew Orleans
Period5/06/186/06/18

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