Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Foundations of statistical natural language processing
Foundations of statistical natural language processing
The String-to-String Correction Problem
Journal of the ACM (JACM)
Snowball: a prototype system for extracting relations from large text collections
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd 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
Memory-Based Language Processing (Studies in Natural Language Processing)
Memory-Based Language Processing (Studies in Natural Language Processing)
Multi-field information extraction and cross-document fusion
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A rote extractor with edit distance-based generalisation and multi-corpora precision calculation
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
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In this paper, we describe an ontology-driven pattern disambiguation process for Rote Extractors. Our approach can generate lexical patterns for a particular relation from unrestricted text. Then patterns can be used to recognize concepts, which have the same relation in other text. We test our experiments with/without the ontology. The results show that our approach can dramatically improve the performance of existing pattern-based Rote Extractors.