WordNet: a lexical database for English
Communications of the ACM
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Question-answering by predictive annotation
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Exploiting redundancy in question answering
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
High performance question/answering
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Ranking suspected answers to natural language questions using predictive annotation
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A method for word sense disambiguation of unrestricted text
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Answering what-is questions by Virtual Annotation
HLT '01 Proceedings of the first international conference on Human language technology research
Question answering using maximum entropy components
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Learning to find answers to questions on the Web
ACM Transactions on Internet Technology (TOIT)
Robust techniques for organizing and retrieving spoken documents
EURASIP Journal on Applied Signal Processing
Open-domain question: answering
Foundations and Trends in Information Retrieval
Educational Question Answering based on Social Media Content
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
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One of the most critical components of a question-answering system is the identification of the type, or semantic class, of the answer sought. Systems today use widely-varying numbers of such classes, but all must map the question to one or more classes in their repertoire. In this paper, we present a statistical method of associating question terms with candidate semantic classes that has been shown to achieve a high degree of accuracy and to be applicable to different underlying semantic classifications.