Answer selection is an important task in question answering (QA) from the Web. To address the intrinsic difficulty in encoding sentences with semantic meanings, we introduce a general framework, i.e., Lexical Semantic Feature based Skip Convolution Neural Network (LSF-SCNN), with several optimization strategies. The intuitive idea is that the granular representations with more semantic features of sentences are deliberately designed and estimated to capture the similarity between question-answer pairwise sentences. The experimental results demonstrate the effectiveness of the proposed strategies and our model outperforms the state-of-the-art ones by up to 3.5% on the metrics of MAP and MRR. Copyright © 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.
|Title of host publication
|Proceedings of the 26th International Conference on World Wide Web, WWW 2017
|Place of Publication
|The Association for Computing Machinery
|Published - 2017