title: Mining association language patterns using a distributional semantic model for negative life event classification.
abstract: OBJECTIVE
  Negative life events, such as the death of a family member, an argument with a spouse or the loss of a job, play an important role in triggering depressive episodes. Therefore, it is worthwhile to develop psychiatric services that can automatically identify such events. This study describes the use of association language patterns, i.e., meaningful combinations of words (e.g., <loss, job>), as features to classify sentences with negative life events into predefined categories (e.g., Family, Love, Work).
  
  
  METHODS
  This study proposes a framework that combines a supervised data mining algorithm and an unsupervised distributional semantic model to discover association language patterns. The data mining algorithm, called association rule mining, was used to generate a set of seed patterns by incrementally associating frequently co-occurring words from a small corpus of sentences labeled with negative life events. The distributional semantic model was then used to discover more patterns similar to the seed patterns from a large, unlabeled web corpus.
  
  
  RESULTS
  The experimental results showed that association language patterns were significant features for negative life event classification. Additionally, the unsupervised distributional semantic model was not only able to improve the level of performance but also to reduce the reliance of the classification process on the availability of a large, labeled corpus.
authors.affiliation: Department of Information Management, Yuan Ze University, Chung-Li, Taiwan, ROC. lcyu@saturn.yzu.edu.tw
authors.forename: Liang-Chih
authors.initials: LC
authors.lastname: Yu
authors.forename: Chien-Lung
authors.initials: CL
authors.lastname: Chan
authors.forename: Chao-Cheng
authors.initials: CC
authors.lastname: Lin
authors.forename: I-Chun
authors.initials: IC
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D003866:Depressive Disorder / Q000175:diagnosis / Q000517:prevention & control*; D006801:Humans; D020407:Internet; D008016:Life Change Events; D008962:Models, Theoretical; D009323:Natural Language Processing; D012660:Semantics; D012944:Social Support