Machine Learning for Information Extraction in Informal Domains
Machine Learning - Special issue on information retrieval
A flexible learning system for wrapping tables and lists in HTML documents
Proceedings of the 11th international conference on World Wide Web
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Closing the gap: learning-based information extraction rivaling knowledge-engineering methods
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
On-demand information extraction
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Language-Independent Set Expansion of Named Entities Using the Web
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Methods for domain-independent information extraction from the web: an experimental comparison
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A probabilistic model of redundancy in information extraction
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
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Classical Information Extraction (IE) systems fill slots in domain-specific frames. This paper reports on SEQ, a novel open IE system that leverages a domain-independent frame to extract ordered sequences such as presidents of the United States or the most common causes of death in the U.S. SEQ leverages regularities about sequences to extract a coherent set of sequences from Web text. SEQ nearly doubles the area under the precision-recall curve compared to an extractor that does not exploit these regularities.