关键词: 中文问答系统, 本体, 浅层句法分析, 最大熵, SPARQL查询

Abstract: In order to improve the accuracy and effectiveness of question semantic recognition in question answering system, this paper presents a semantic analyzing algorithm combining shallow parsing and the maximum entropy on the basis of constructing biomedical domain ontology. Firstly, natural language questions are identified by semantic blocks. If the recognition is successful, the question vectors are formed, and then the SPARQL query is performed on the ontology. Otherwise, the maximum entropy model is invoked to judge the semantic role of the question. The maximum entropy model is used to train annotated corpus, which extracts the semantic block features to determine the most probable sentence pattern and form question vector, and then query through ontology to get the answers. Finally, compared with other methods, the novel algorithm has higher precision and recall rate.

Key words:  Chinese question answering system, ontology, shallow parsing, maximum entropy, SPARQL query