Discovery procedures for sublanguage selectional patterns: initial experiments
Computational Linguistics
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
Computational Linguistics - Special issue on using large corpora: I
A non-projective dependency parser
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Contextual word similarity and estimation from sparse data
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
University of Massachusetts: MUC-4 test results and analysis
MUC4 '92 Proceedings of the 4th conference on Message understanding
Description of the UMass system as used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
A maximum entropy approach to information extraction from semi-structured and free text
Eighteenth national conference on Artificial intelligence
Information extraction from free-text business documents
Effective databases for text & document management
Automatic acquisition of domain knowledge for Information Extraction
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Automatic pattern acquisition for Japanese information extraction
HLT '01 Proceedings of the first international conference on Human language technology research
Unsupervised personal name disambiguation
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Expressing implicit semantic relations without supervision
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
An alignment-based pattern representation model for information extraction
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
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Natural Language Engineering
Improved large margin dependency parsing via local constraints and laplacian regularization
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
A task-based comparison of information extraction pattern models
DeepLP '07 Proceedings of the Workshop on Deep Linguistic Processing
Ontology-based information extraction for business intelligence
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Multi-class named entity recognition via bootstrapping with dependency tree-based patterns
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A local tree alignment approach to relation extraction of multiple arguments
Information Processing and Management: an International Journal
Learning non-taxonomical semantic relations from domain texts
Journal of Intelligent Information Systems
Automatic relation extraction with model order selection and discriminative label identification
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
A hybrid approach for relation extraction aimed at the semantic web
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
Bootstrapping events and relations from text
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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Information Extraction (IE) systems are commonly based on pattern matching. Adapting an IE system to a new scenario entails the construction of a new pattern base---a time-consuming and expensive process. We have implemented a system for finding patterns automatically from un-annotated text. Starting with a small initial set of seed patterns proposed by the user, the system applies an incremental discovery procedure to identify new patterns. We present experiments with evaluations which show that the resulting patterns exhibit high precision and recall.