An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
HLT '91 Proceedings of the workshop on Speech and Natural Language
Collective information extraction with relational Markov networks
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Factorizing complex models: a case study in mention detection
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
BestCut: a graph algorithm for coreference resolution
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Accurate semantic class classifier for coreference resolution
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
A semi-supervised approach to extracting multiword entity names from user reviews
Proceedings of the 1st Joint International Workshop on Entity-Oriented and Semantic Search
Mining entity types from query logs via user intent modeling
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
No noun phrase left behind: detecting and typing unlinkable entities
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Research in named entity recognition and mention detection has typically involved a fairly small number of semantic classes, which may not be adequate if semantic class information is intended to support natural language applications. Motivated by this observation, we examine the under-studied problem of semantic subtype induction, where the goal is to automatically determine which of a set of 92 fine-grained semantic classes a noun phrase belongs to. We seek to improve the standard supervised approach to this problem using two techniques: hierarchical classification and collective classification. Experimental results demonstrate the effectiveness of these techniques, whether or not they are applied in isolation or in combination with the standard approach.