Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Unsupervised discovery of scenario-level patterns for Information Extraction
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A self-learning universal concept spotter
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
New York University: description of the Proteus system as used for MUC-5
MUC5 '93 Proceedings of the 5th conference on Message understanding
Automatic pattern acquisition for Japanese information extraction
HLT '01 Proceedings of the first international conference on Human language technology research
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
HLT '91 Proceedings of the workshop on Speech and Natural Language
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
Bootstrapping events and relations from text
Bootstrapping events and relations from text
Using document level cross-event inference to improve event extraction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Using cross-entity inference to improve event extraction
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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In this paper, we describe a new approach to semi-supervised adaptive learning of event extraction from text. Given a set of examples and an un-annotated text corpus, the BEAR system (Bootstrapping Events And Relations) will automatically learn how to recognize and understand descriptions of complex semantic relationships in text, such as events involving multiple entities and their roles. For example, given a series of descriptions of bombing and shooting incidents (e.g., in newswire) the system will learn to extract, with a high degree of accuracy, other attack-type events mentioned elsewhere in text, irrespective of the form of description. A series of evaluations using the ACE data and event set show a significant performance improvement over our baseline system.