A WSD method is presented, using domain keywords and word vector model built from unlabelled data. The effectiveness of the proposed approach is proved, compared with other WSD methods including Lesk on evaluation corpus in environmental domain. Through employing knowledge from different fields, proposed method can be adapted into the WSD task of other domains.

In search services, users can get information more conveniently by reading the succinct answers to their questions. This paper introduces a feature-based method for the query-focused summarization to extract the answer summary of a user query. A convolutional neural network (CNN) is used to learn the semantic representation of a sentence, by which the similarity between a candidate answer sentence and a user query is evaluated. The neural network is trained under the framework of max-margin learning. Experiments in Baidu Knows verify that the proposed method can generate the concise answer of a user query.