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
Relational learning of pattern-match rules for information extraction
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
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
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Information Extraction with HMM Structures Learned by Stochastic Optimization
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Using the web to obtain frequencies for unseen bigrams
Computational Linguistics - Special issue on web as corpus
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Automatic acquisition of domain knowledge for Information Extraction
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Mining the Web for bilingual text
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Overview of the fourth message understanding evaluation and conference
MUC4 '92 Proceedings of the 4th conference on Message understanding
Overview of results of the MUC-6 evaluation
MUC6 '95 Proceedings of the 6th conference on Message understanding
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
CRYSTAL inducing a conceptual dictionary
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
The design, implementation, and use of the Ngram statistics package
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Creating subjective and objective sentence classifiers from unannotated texts
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
A bootstrapping approach for identifying stakeholders in public-comment corpora
dg.o '07 Proceedings of the 8th annual international conference on Digital government research: bridging disciplines & domains
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Mutual Screening Graph Algorithm: A New Bootstrapping Algorithm for Lexical Acquisition
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
Sketching techniques for large scale NLP
WAC-6 '10 Proceedings of the NAACL HLT 2010 Sixth Web as Corpus Workshop
Journal of Artificial Intelligence Research
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Many information extraction (IE) systems rely on manually annotated training data to learn patterns or rules for extracting information about events. Manually annotating data is expensive, however, and a new data set must be annotated for each domain. So most IE training sets are relatively small. Consequently, IE patterns learned from annotated training sets often have limited coverage. In this paper, we explore the idea of using the Web to automatically identify domain-specific IE patterns that were not seen in the training data. We use IE patterns learned from the MUC-4 training set as anchors to identify domain-specific web pages and then learn new IE patterns from them. We compute the semantic affinity of each new pattern to automatically infer the type of information that it will extract. Experiments on the MUC-4 test set show that these new IE patterns improved recall with only a small precision loss.