Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Bayesian information extraction network
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Peeling back the layers: detecting event role fillers in secondary contexts
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Bootstrapped training of event extraction classifiers
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Discourse structure and language technology
Natural Language Engineering
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Hidden Markov models (HMMs) are powerful statistical models that have found successful applications in Information Extraction (IE). In current approaches to applying HMMs to IE, an HMM is used to model text at the document level. This modelling might cause undesired redundancy in extraction in the sense that more than one filler is identified and extracted. We propose to use HMMs to model text at the segment level, in which the extraction process consists of two steps: a segment retrieval step followed by an extraction step. In order to retrieve extraction-relevant segments from documents, we introduce a method to use HMMs to model and retrieve segments. Our experimental results show that the resulting segment HMM IE system not only achieves near zero extraction redundancy, but also has better overall extraction performance than traditional document HMM IE systems.