J Biomed Inform - Mining association language patterns using a distributional semantic model for negative life event classification.

Tópicos

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Resumo

RPOSE: 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.

Resumo Limpo

rpose negat life event death famili member argument spous loss job play import role trigger depress episod therefor worthwhil develop psychiatr servic can automat identifi event studi describ use associ languag pattern ie meaning combin word eg loss job featur classifi sentenc negat life event predefin categori eg famili love workmethod studi propos framework combin supervis data mine algorithm unsupervis distribut semant model discov associ languag pattern data mine algorithm call associ rule mine use generat set seed pattern increment associ frequent cooccur word small corpus sentenc label negat life event distribut semant model use discov pattern similar seed pattern larg unlabel web corpusresult experiment result show associ languag pattern signific featur negat life event classif addit unsupervis distribut semant model abl improv level perform also reduc relianc classif process avail larg label corpus

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