Abstract
Recently, automatic QA pair generation has been an essential technique to reduce human involvement in the construction of QA systems. In a big data era, huge information is produced every day. Therefore, it is an important issue for QA systems to be able to respond to users with up-to-date information, e.g., to answer questions regarding recent posts on blogs. The major problem in building such systems is the efficiency to capture relevant text sources for specific QA domains. In this study, topic modeling is used as a means to help determine efficiently if an article is of the same topic as a specific domain of interest, e.g., health domain as exemplified in this paper. QA pairs are then generated from these selected articles using the proposed sentence extraction method. Experimental results show that, using the proposed method with topic modeling, a 7.3 % acceptance rate improvement on the generated questions was achieved.
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