Tohoku at SemEval-2016 task 6: Feature-based model versus convolutional neural network for stance detection

Yuki Igarashi, Hiroya Komatsu, Sosuke Kobayashi, Naoaki Okazaki, Kentaro Inui

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

7 Citations (Scopus)

Abstract

In this paper, we compare feature-based and Neural Network-based approaches on the supervised stance classification task for tweets in SemEval-2016 Task 6 Subtask A (Mohammad et al., 2016). In the feature-based approach, we use external resources such as lexicons and crawled texts. The Neural Network based approach employs Convolutional Neural Network (CNN). Our results show that the feature-based model outperformed the CNN model on the test data although the CNN model was better than the feature-based model in the cross validation on the training data.

Original languageEnglish
Title of host publicationSemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages401-407
Number of pages7
ISBN (Electronic)9781941643952
DOIs
Publication statusPublished - 2016
Event10th International Workshop on Semantic Evaluation, SemEval 2016 - San Diego, United States
Duration: 2016 Jun 162016 Jun 17

Publication series

NameSemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings

Other

Other10th International Workshop on Semantic Evaluation, SemEval 2016
CountryUnited States
CitySan Diego
Period16/6/1616/6/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Computer Science Applications

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  • Cite this

    Igarashi, Y., Komatsu, H., Kobayashi, S., Okazaki, N., & Inui, K. (2016). Tohoku at SemEval-2016 task 6: Feature-based model versus convolutional neural network for stance detection. In SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings (pp. 401-407). (SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s16-1065