Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Exploiting redundancy in question answering
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Open-domain textual question answering techniques
Natural Language Engineering
A new statistical parser based on bigram lexical dependencies
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
The role of lexico-semantic feedback in open-domain textual question-answering
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Is it the right answer?: exploiting web redundancy for Answer Validation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
MAYA: a fast Question-answering system based on a predictive answer indexer
ODQA '01 Proceedings of the workshop on Open-domain question answering - Volume 12
Parsing and question classification for question answering
ODQA '01 Proceedings of the workshop on Open-domain question answering - Volume 12
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Question answering (QA) is a relatively new area of research. We took the approach of designing a question answering system that is based on question classification and document tagging. Question classification extracts useful information from the question about how to answer the question. Document tagging extracts useful information from the documents, which are used to find the answer to the question. We used different available systems to tag the documents. Our system classifies the questions using manually developed rules. An evaluation of the system is performed using Text REtrieval Conference (TREC) data.