Using an analogical reasoning framework to infer language patterns for negative life events.

Clicks: 217
ID: 30469
2019
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Abstract
Feelings of depression can be caused by negative life events (NLE) such as the death of a family member, a quarrel with one's spouse, job loss, or strong criticism from an authority figure. The automatic and accurate identification of negative life event language patterns (NLE-LP) can help identify individuals potentially in need of psychiatric services. An NLE-LP combines a person (subject) and a reasonable negative life event (action), e.g. or < boyfriend:break_up>.This paper proposes an analogical reasoning framework which combines a word representation approach and a pattern inference method to mine/extract NLE-LPs from psychiatric consultation documents. Word representation approaches such as skip-gram (SG) and continuous bag-of-words (CBOW) are used to generate word embeddings. Pattern inference methods such as cosine similarity (COSINE) and cosine multiplication similarity (COSMUL) are used to infer patterns.Experimental results show our proposed analogical reasoning framework outperforms the traditional methods such as positive pairwise mutual information (PPMI) and hyperspace analog to language (HAL), and can effectively mine highly precise NLE-LPs based on word embeddings. CBOW with COSINE of analogical reasoning is the best word representation and inference engine. In addition, both word embeddings and the inference engine provided by the analogical reasoning framework can further be used to improve the HAL model.Our proposed framework is a very simple matching function based on these word representation approaches and is applied to significantly improve HAL model mining performance.
Reference Key
wu2019usingbmc Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wu, Jheng-Long;Xiao, Xiang;Yu, Liang-Chih;Ye, Shao-Zhen;Lai, K Robert;
Journal BMC medical informatics and decision making
Year 2019
DOI
10.1186/s12911-019-0895-8
URL
Keywords Keywords not found

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