caa a22 4500 605468281 CHVBK 20210128100316.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00500-014-1386-6 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1386-6 Sentence extraction with topic modeling for question-answer pair generation [Elektronische Daten] [Chung-Hsien Wu, Chao-Hong Liu, Po-Hsun Su] 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. Springer-Verlag Berlin Heidelberg, 2014 Sentence extraction nationallicence Holism-based detection nationallicence QA pair generation nationallicence Topic modeling nationallicence Wu Chung-Hsien Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan aut Liu Chao-Hong ICL, Speech, Language and Audio Processing Department, Industrial Technology Research Institute, Hsinchu, Taiwan aut Su Po-Hsun Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan aut Soft Computing Springer Berlin Heidelberg 19/1(2015-01-01), 39-46 1432-7643 19:1<39 2015 19 500 https://doi.org/10.1007/s00500-014-1386-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1386-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wu Chung-Hsien Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan aut NATIONALLICENCE 700 1- Liu Chao-Hong ICL, Speech, Language and Audio Processing Department, Industrial Technology Research Institute, Hsinchu, Taiwan aut NATIONALLICENCE 700 1- Su Po-Hsun Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/1(2015-01-01), 39-46 1432-7643 19:1<39 2015 19 500