GM-RKB:2003 EarlyResultsForNERwithCRFs

• (McCallum & Li, 2003) => Andrew McCallum, Wei Li. (2003). "Early Results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons." In: Proceedings of Seventh Conference on Natural Language Learning (CoNLL 2003). [doi>10.3115/1119176.1119206]

Subject Heading(s): Named Entity Recognition Algorithm, CRF-based NER Algorithm.

Notes

• It is an early (possibly the first?) paper on an Supervised NER Algorithm that is a CRF Algorithm.
• It uses a Linear Chain CRF.

Cited by

~240 http://scholar.google.ca/scholar?cites=5372834304744483107

2003

• (Tjong Kim Sang & de Meulder, 2003) => Erik Tjong Kim Sang, and F. De Meulder. (2003). "Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition." In: Proceedings of Conference on Natural Language Learning (CoNLL 2003).

Quotes

Abstract

• Models for many natural language tasks benefit from the flexibility to use overlapping, non-independent features. For example, the need for labeled data can be drastically reduced by taking advantage of domain knowledge in the form of word lists, part-of-speech tags, character n-grams, and capitalization patterns. While it is difficult to capture such inter-dependent features with a generative probabilistic model, conditionally-trained models, such as conditional maximum entropy models, handle them well. There has been significant work with such models for greedy sequence modeling in NLP (Ratnaparkhi, 1996; Borthwick et al., 1998).

2. Conditional Random Fields

• Conditional Random Fields (CRFs) (Lafferty et al., 2001) are undirected graphical models used to calculate the conditional probability of values on designated output nodes given values assigned to other designated input nodes.
• In the special case in which the output nodes of the graphical model] are linked by edges in a linear chain, CRFs make a first-order Markov independence assumption, and thus can be understood as conditionally-trained finite state machines (FSMs). In the remainder of this section we introduce the likelihood model, inference and estimation procedures for CRFs.

References

• A. Borthwick, J. Sterling, E. Agichtein, and R. Grishman. 1998. Exploiting diverse knowledge sources via maximum entropy in named entity recognition. In Proceedings of the Sixth Workshop on Very Large Corpora, Association for Computational Linguistics.
• M. Collins and Y. Singer. 1999. Unsupervised models for named entity classification. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora.
• Stephen Della Pietra, Vincent Della Pietra , John Lafferty, Inducing Features of Random Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.19 n.4, p.380-393, April 1997 [doi>10.1109/34.588021]
• Rosie Jones, Andrew McCallum, Kamal Nigam, and Ellen Riloff. 1999. Bootstrapping for Text Learning Tasks. In IJCAI-99 Workshop on Text Mining: Foundations, Techniques and Applications.
• John D. Lafferty , Andrew McCallum, Fernando C. N. Pereira, Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, Proceedings of the Eighteenth International Conference on Machine Learning, p.282-289, June 28-July 01, 2001
• Robert Malouf, A comparison of algorithms for maximum entropy parameter estimation, proceeding of the 6th conference on Natural language learning, p.1-7, August 31, 2002 [doi>10.3115/1118853.1118871]
• Andrew McCallum and Fang-Fang Feng. 2003. Chinese Word Segmentation with Conditional Random Fields and Integrated Domain Knowledge. In Unpublished Manuscript.
• Andrew McCallum. 2003. Efficiently Inducing Features of Conditional Random Fields. In Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI03). (Submitted).
• Adwait Ratnaparkhi. 1996. A Maximum Entropy Model for Part-of-Speech Tagging. In Eric Brill and Kenneth Church, editors, Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 133--142. Association for Computational Linguistics.
• Fei Sha, Fernando Pereira, Shallow parsing with conditional random fields, Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, p.134-141, May 27-June 01, 2003, Edmonton, Canada [doi>10.3115/1073445.1073473]

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2003 EarlyResultsForNERwithCRFs				Early Results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons.			http://www.cs.umass.edu/~mccallum/papers/mccallum-conll2003.pdf	10.3115/1119176.1119206