TY - EJOU AU - Zhang, Bo AU - Wang, Haowen AU - Jiang, Longquan AU - Yuan, Shuhan AU - Li, Meizi TI - A Novel Bidirectional LSTM and Attention Mechanism Based Neural Network for Answer Selection in Community Question Answering T2 - Computers, Materials \& Continua PY - 2020 VL - 62 IS - 3 SN - 1546-2226 AB - Deep learning models have been shown to have great advantages in answer selection tasks. The existing models, which employ encoder-decoder recurrent neural network (RNN), have been demonstrated to be effective. However, the traditional RNN-based models still suffer from limitations such as 1) high-dimensional data representation in natural language processing and 2) biased attentive weights for subsequent words in traditional time series models. In this study, a new answer selection model is proposed based on the Bidirectional Long Short-Term Memory (Bi-LSTM) and attention mechanism. The proposed model is able to generate the more effective question-answer pair representation. Experiments on a question answering dataset that includes information from multiple fields show the great advantages of our proposed model. Specifically, we achieve a maximum improvement of 3.8% over the classical LSTM model in terms of mean average precision. KW - Question answering KW - answer selection KW - deep learning KW - Bi-LSTM KW - attention mechanisms DO - 10.32604/cmc.2020.07269