Disease Event Detection based on Deep Modality Analysis

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.

• Type: Journal
• Year: 2015
• Where: ACL
• Authors: Yoshiaki Kitagawa, Mamoru Komachi, Eiji Aramaki, Naoaki Okazaki, Hiroshi Ishikawa 0004
• Keywords: ACL 2015, Computational Linguistics