C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
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
A maximum entropy approach to information extraction from semi-structured and free text
Eighteenth national conference on Artificial intelligence
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
GE NLToolset: MUC-4 test results and analysis
MUC4 '92 Proceedings of the 4th conference on Message understanding
Named entity recognition: a maximum entropy approach using global information
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Description of the UMass system as used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
Automatic labeling of semantic roles
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Adaptive information extraction from text by rule induction and generalisation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Relational learning via propositional algorithms: an information extraction case study
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Mining information extraction rules from datasheets without linguistic parsing
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Adaptive information extraction
ACM Computing Surveys (CSUR)
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Discriminative slot detection using kernel methods
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Exploiting subjectivity classification to improve information extraction
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Learning domain-specific information extraction patterns from the Web
IEBeyondDoc '06 Proceedings of the Workshop on Information Extraction Beyond The Document
A unified model of phrasal and sentential evidence for information extraction
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Fuzzy pattern rule induction for information extraction
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Extracting sequences from the web
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Template-based information extraction without the templates
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
An ontology-based information extraction approach for résumés
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Hi-index | 0.00 |
In this paper, we present a learning approach to the scenario template task of information extraction, where information filling one template could come from multiple sentences. When tested on the MUC-4 task, our learning approach achieves accuracy competitive to the best of the MUC-4 systems, which were all built with manually engineered rules. Our analysis reveals that our use of full parsing and state-of-the-art learning algorithms have contributed to the good performance. To our knowledge, this is the first research to have demonstrated that a learning approach to the full-scale information extraction task could achieve performance rivaling that of the knowledge engineering approach.