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

Abstract:

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., ), 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.

journal_name

J Biomed Inform

authors

Yu LC,Chan CL,Lin CC,Lin IC

doi

10.1016/j.jbi.2011.01.006

subject

Has Abstract

pub_date

2011-08-01 00:00:00

pages

509-18

issue

4

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(11)00008-6

journal_volume

44

pub_type

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