Disease event detection based on deep modality analysis

Yoshiaki Kitagawa, Mamoru Komachi, Eiji Aramaki, Naoaki Okazaki, Hiroshi Ishikawa

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

7 Citations (Scopus)

Abstract

Social media has attracted attention because of its potential for extraction of information of various types. For example, information collected from Twitter enables us to build useful applications such as predicting an epidemic of influenza. However, using text information from social media poses challenges for event detection because of the unreliable nature of user-generated texts, which often include counter-factual statements. Consequently, this study proposes the use of modality features to improve disease event detection from Twitter messages, or "tweets". Experimental results demonstrate that the combination of a modality dictionary and a modality analyzer improves the F1-score by 3.5 points.

Original languageEnglish
Title of host publicationACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Proceedings of the Student Research Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages28-34
Number of pages7
ISBN (Electronic)9781941643747
DOIs
Publication statusPublished - 2015
Event53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2015 - Beijing, China
Duration: 2015 Jul 28 → …

Publication series

NameACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Proceedings of the Student Research Workshop

Other

Other53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2015
CountryChina
CityBeijing
Period15/7/28 → …

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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