Relevance feedback with too much data
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Can We Make Information Extraction More Adaptive?
Information Extraction: Towards Scalable, Adaptable Systems
Bottom-up relational learning of pattern matching rules for information extraction
The Journal of Machine Learning Research
A non-projective dependency parser
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Automatic acquisition of domain knowledge for Information Extraction
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Counter-training in discovery of semantic patterns
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
A bootstrapping method for learning semantic lexicons using extraction pattern contexts
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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We report on a set of experiments in text mining, specifically, finding semantic patterns given only a few keywords. The experiments employ the Counter-training framework for discovery of semantic knowledge from raw text in a weakly supervised fashion. The experiments indicate that the framework is suitable for efficient acquisition of semantic word classes and collocation patterns, which may be used for Information Extraction.