Scaling question answering to the Web
Proceedings of the 10th international conference on World Wide Web
Learning search engine specific query transformations for question answering
Proceedings of the 10th international conference on World Wide Web
Getting answers to natural language questions on the web
Journal of the American Society for Information Science and Technology
Probabilistic question answering on the web
Proceedings of the 11th international conference on World Wide Web
Semantic computation in a Chinese question-answering system
Journal of Computer Science and Technology
Improving Category Specific Web Search by Learning Query Modifications
SAINT '01 Proceedings of the 2001 Symposium on Applications and the Internet (SAINT 2001)
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Traditional Chinese text retrieval systems return a ranked list of documents in response to a user's request. While a ranked list of documents may be an appropriate response for the user, frequently it is not. Usually it would be better for the system to provide the answer itself instead of requiring the user to search for the answer in a set of documents. Since Chinese text retrieval has just been developed lately, and due to various specific characteristics of Chinese language, the approaches to its retrieval are quite different from those studies and researches proposed to deal with Western language. Thus, an architecture that augments existing search engines is developed to support Chinese natural language question answering. In this paper a new approach to building Chinese question-answering system is described, which is the general-purpose, fully-automated Chinese question-answering system available on the web. In the approach, we attempt to represent Chinese text by its characteristics, and try to convert the Chinese text into ERE (E: entity, R: relation) relation data lists, and then to answer the question through ERE relation model. The system performs quite well giving the simplicity of the techniques being utilized. Experimental results show that question-answering accuracy can be greatly improved by analyzing more and more matching ERE relation data lists. Simple ERE relation data extraction techniques work well in our system making it efficient to use with many backend retrieval engines.