Answer Selection Based on Aligned Local Composite Features and Global Features

With the rapid increase of text information on the Internet, intelligent question answering has attracted considerable attention in many different domains. Many studies use intelligent question answering as an answer selection task and use local or global features to encode sentence representation, which has been proven to be very effective. However, due to the different characteristics of the corpora in different domains or languages, these methods usually suffer a great reduction in performance and cannot produce a sentence representation with good qualities. In this paper, we introduce Hybrid Model combining Local Composite features and Global features into a Siamese network (HM-LCGS) to alleviate the issue. The framework consists of a novel convolution neural network like architecture called local composite features convolution neural network to extract sufficient semantic information of the text from different granularity, shortcut connection to combine local composite features into pre-trained embeddings, alignment layer to mine the correlation between question and answer and bidirectional Long Short-Term Memory to encode the final sentence representation. The experimental results on InsuranceQA and cMedQA datasets show that with suitable granularity selection and embedding method, our proposed model can achieve competitive performance compared with other state-of-the-art models.