ASELM: Adaptive semi-supervised ELM with application in question subjectivity identification

Abstract

Question subjectivity identification in Community Question Answering (CQA) has attracted a lot of attentions in recent years. With the rapid development of CQA, subjective questions posted by users are growing exponentially, which presents two challenges for question subjectivity identification. The first one is the data imbalance between subjective and objective questions. The second one is that the amount of manually labelled training data is hard to catch up with the fast developing speed of CQA. In this paper, we propose an adaptive semi-supervised Extreme Learning Machine (ASELM) to solve those two challenges. To resolve the data imbalance problem, ASELM employs the different impacts on identification performance caused by the imbalanced data. Second, the proposed method introduces the unlabelled data, and builds a model about the ratio between the number of labelled and unlabelled data based on Gaussian Model, which is applied to automatically generate the constraint on the unlabelled data. Experimental results showed ASELM improved identification performance for the imbalanced data, and outperformed the performance of basic ELM, SELM, Weighted ELM and SS-ELM on both F1 measure and accuracy.