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Information Processing and Management: an International Journal - Special issue on natural language processing and information retrieval
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Information Processing and Management: an International Journal
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Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing
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CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Answer Extraction in Technical Domains
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
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Computational Linguistics - Special issue on using large corpora: I
Another look at nominal compounds
ACL '84 Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics
Semi-automatic recognition of noun modifier relationships
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Terminological variation, a means of identifying research topics from texts
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Processing complex noun phrases in a natural language interface to a statistical database
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
Exploiting paraphrases in a Question Answering system
PARAPHRASE '03 Proceedings of the second international workshop on Paraphrasing - Volume 16
A robust and hybrid deep-linguistic theory applied to large-scale parsing
ROMAND '04 Proceedings of the 3rd Workshop on RObust Methods in Analysis of Natural Language Data
A term normalization method for better performance of terminology construction
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
A term normalization method for efficient knowledge acquisition through text processing
Multimedia Tools and Applications
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Question Answering provides a method of locating precise answers to specific questions but in technical domains the amount of Multi-Word Terms complicates this task.This paper outlines the Question Answering task in such a domain and explores two ways of detecting relations between Multi-Word Terms. The first targets specific semantic relations, the second uses a clustering algorithm, but they are both based on the idea of syntactic variation. The paper demonstrates how the combination of these two methodologies provide sophisticated access to technical domains.