Learning search engine specific query transformations for question answering
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Question Answering Using Unification-Based Grammar
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Natural language question answering: the view from here
Natural Language Engineering
A weighted robust parsing approach to semantic annotation
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Learning to find answers to questions on the Web
ACM Transactions on Internet Technology (TOIT)
Answer extraction towards better evaluations of NLP systems
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Using semantic templates for a natural language interface to the CINDI virtual library
Data & Knowledge Engineering - Special issue: Natural language and database and information systems: NLDB 03
1 Billion Pages = 1 Million Dollars? mining the web to play "who wants to be a millionaire?"
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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In this paper we describe ExtrAns, an answer extraction system. Answer extraction (AE) aims at retrieving those exact passages of a document that directly answer a given user question. AE is more ambitious than information retrieval and information extraction in that the retrieval results are phrases, not entire documents, and in that the queries may be arbitrarily specific. It is less ambitious than full-fledged question answering in that the answers are not generated from a knowledge base but looked up in the text of documents. The current version of ExtrAns is able to parse unedited Unix ``man pages'', and derive the logical form of their sentences. User queries are also translated into logical forms. A theorem prover then retrieves the relevant phrases, which are presented through selective highlighting in their context.