Query generation using semantic features

TL;DR: A query generation using semantic features to represent the information demand of users for question answering and information retrieval is described and an efficient document retrieval is possible by a question analysis based on semantic features on natural language questions which are comparatively short but fully expressing the information demands of users.

Abstract: This paper describes a query generation using semantic features to represent the information demand of users for question answering and information retrieval. One of fundamental reasons why unwanted results are included in responses of all information retrieval systems is because queries do not exactly represent the information demand of users. To solve this problem, a query generaton using the semantic feature is intended to extract semantic features which appear commonly in natural language questions of similar type and utilize them for question answering and information retrieval. We extract semantic features from natural language questions using a grammar and generate queries which represent enough information demands of users using semantic features and syntactic structures. For performance improvement of question answering and information retrieval, we introduce a query-document similarity used to rank documents which include generated queries in the high position. We evaluated our mechanism using 100 queries about a person in the web. There was a notable improvement in the precision at N documents when our approach is applied. Especially, we found that an efficient document retrieval is possible by a question analysis based on semantic features on natural language questions which are comparatively short but fully expressing the information demand of users.