Andrew McCallum
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).
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- Stephen Della Pietra, Vincent J. Della Pietra, and John D. Lafferty. 1997. Inducing Features of Random Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(4):380--393. Google ScholarDigital Library
- 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.Google Scholar
- John Lafferty, Andrew McCallum, and Fernando Pereira. 2001. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proc. ICML. Google ScholarDigital Library
- Robert Malouf. 2002. A comparison of algorithms for maximum entropy parameter estimation. In Sixth Workshop on Computational Language Learning (CoNLL-2002). Google ScholarDigital Library
- Andrew McCallum and Fang-Fang Feng. 2003. Chinese Word Segmentation with Conditional Random Fields and Integrated Domain Knowledge. In Unpublished Manuscript.Google Scholar
- Andrew McCallum. 2003. Efficiently Inducing Features of Conditional Random Fields. In Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI03). (Submitted). Google ScholarDigital Library
- 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.Google Scholar
- Fei Sha and Fernando Pereira. 2003. Shallow Parsing with Conditional Random Fields. In Proceedings of Human Language Technology, NAACL. Google ScholarDigital Library
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