Abstract
Online CQA (community question-answering) systems are a hot topic today. Users can freely ask and answer questions in any subject with little restrictions. Such communities are a good resource for automatic question-answering systems, providing resourceful natural-language question-answer pairs with sub-attributes like rankings, scores, likes and so forth for evaluation. Traditional question retrieval engines use the same word for searching but neglect similar expressions, which may lead to lexical gap, omitting results with similar expressions. In this article, we build a question-answering system in Chinese based on corpuses selected from online Chinese cQAs by selecting the most similar query in the database with trained word embedding. Results show our method performs better in bridging lexical gap than traditional inverted index.
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