Detecting causality from online psychiatric texts using inter-sentential language patterns

• Published in: BMC medical informatics and decision making 2012-07, Vol.12 (1), p.72-72, Article 72
• Main Authors: Wu, Jheng-Long, Yu, Liang-Chih, Chang, Pei-Chann
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Biomedical text mining; Boundaries; Causality; Causality detection; Computational linguistics; Data mining; Data Mining - methods; Databases, Factual; Decision making; Depressive Disorder - epidemiology; Depressive Disorder - prevention & control; Humans; Informatics; Information Storage and Retrieval - trends; Inter-sentential language patterns; Internet; Language; Language processing; Life Change Events; Medical informatics; Medical personnel; Mental disorders; Mental health; Mineral industry; Mining industry; Natural language; Natural language interfaces; Natural language processing; Psychiatric services; Semantics; Social networks; Virtual communities; Weblogs
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/1472-6947-12-72

Online psychiatric texts are natural language texts expressing depressive problems, published by Internet users via community-based web services such as web forums, message boards and blogs. Understanding the cause-effect relations embedded in these psychiatric texts can provide insight into the authors' problems, thus increasing the effectiveness of online psychiatric services. Previous studies have proposed the use of word pairs extracted from a set of sentence pairs to identify cause-effect relations between sentences. A word pair is made up of two words, with one coming from the cause text span and the other from the effect text span. Analysis of the relationship between these words can be used to capture individual word associations between cause and effect sentences. For instance, (broke up, life) and (boyfriend, meaningless) are two word pairs extracted from the sentence pair: "I broke up with my boyfriend. Life is now meaningless to me". The major limitation of word pairs is that individual words in sentences usually cannot reflect the exact meaning of the cause and effect events, and thus may produce semantically incomplete word pairs, as the previous examples show. Therefore, this study proposes the use of inter-sentential language patterns such as ≪broke up, boyfriend>,