Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
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 discovery of scenario-level patterns for Information Extraction
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Unsupervised learning of generalized names
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Ranking algorithms for named-entity extraction: boosting and the voted perceptron
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
An improved extraction pattern representation model for automatic IE pattern acquisition
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
On-demand information extraction
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Comparing information extraction pattern models
IEBeyondDoc '06 Proceedings of the Workshop on Information Extraction Beyond The Document
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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Named Entity Recognition (NER) has become a well-known problem with many important applications, such as Question Answering, Relation Extraction and Concept Retrieval. NER based on unsupervised learning via bootstrapping is gaining researchers' interest these days because it does not require manually annotating training data. Meanwhile, dependency tree-based patterns have proved to be effective in Relation Extraction. In this paper, we demonstrate that the use of dependency trees as extraction patterns, together with a bootstrapping framework, can improve the performance of the NER system and suggest a method for efficiently computing these tree patterns. Since unsupervised NER via bootstrapping uses the entities learned from each iteration as seeds for the next iterations, the quality of these seeds greatly affects the entire learning process. We introduce the technique of simultaneous bootstrapping of multiple classes, which can dramatically improve the quality of the seeds obtained at each iteration and hence increase the quality of the final learning results. Our experiments show beneficial results.