Weifeng Ma*, Jiao Lou, Caoting Ji, Laibin Ma
CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 179-193, 2021, DOI:10.32604/cmc.2020.011969
- 30 October 2020
Abstract Given the limitations of the community question answering (CQA)
answer quality prediction method in measuring the semantic information of the
answer text, this paper proposes an answer quality prediction model based on
the question-answer joint learning (ACLSTM). The attention mechanism is used
to obtain the dependency relationship between the Question-and-Answer (Q&A)
pairs. Convolutional Neural Network (CNN) and Long Short-term Memory Network (LSTM) are used to extract semantic features of Q&A pairs and calculate
their matching degree. Besides, answer semantic representation is combined with
other effective extended features as the input representation of the fully connected More >
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