Mining association language patterns using a distributional semantic model for negative life event classification

  • Liang-Chih Yu
  • Chien-Lung Chan
  • Chao-Cheng Lin
 et al. 
  • 1


    Mendeley users who have this article in their library.
  • N/A


    Citations of this article.


Purpose: 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. [All rights reserved Elsevier].

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Liang-Chih Yu

  • Chien-Lung Chan

  • Chao-Cheng Lin

  • I-Chun Lin

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free