Lifelong Learning Maxent for Suggestion Classification

Thi-Lan Ngo, Tu Vu, Hideaki Takeda, Son Bao Pham, Xuan Hieu Phan


Suggestion classification for opinion data is defined as identifying a given utterance by suggestion or non-suggestion class. In this paper, we introduce a method called LLMaxent which is the solution for the cross-domain suggestion classification. LLMaxent is a lifelong machine learning approach using maximum entropy (Maxent). In the course of lifelong learning, the drawn knowledge from the past tasks is retained and supported for the future learning. From that, we build a classifier by using labelled data in existed domains for suggestion classification in a new domain. The experimental results show that the proposed novel model can improve the performance of cross-domain suggestion classification. This is one of the preliminary research in lifelong machine learning using Maxent. Its effect is not only for suggestion classification but also for cross-domain text classification in general.


Suggestion mining, cross-domain suggestion classification, lifelong learning, maximum entropy

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