Jinying Chen
ABSTRACT
This paper shows that two uncertainty-based active learning methods, combined with a maximum entropy model, work well on learning English verb senses. Data analysis on the learning process, based on both instance and feature levels, suggests that a careful treatment of feature extraction is important for the active learning to be useful for WSD. The overfitting phenomena that occurred during the active learning process are identified as classic overfitting in machine learning based on the data analysis.
- Naoki Abe and Hiroshi Mamitsuka. 1998. Query learning strategies using boosting and bagging. In Proc. of ICML 1998, pages 1--10. Google Scholar
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- Jinying Chen and Martha Palmer. 2005. Towards Robust High Performance Word Sense Disambiguation of English Verbs Using Rich Linguistic Features, In Proc. of IJCNLP 2005, Oct., Jeju, Republic of Korea.Google ScholarDigital Library
- Tim Chklovski and Rada Mihalcea, Building a Sense Tagged Corpus with Open Mind Word Expert, in Proceedings of the ACL 2002 Workshop on "Word Sense Disambiguation: Recent Successes and Future Directions", Philadelphia, July 2002. Google ScholarDigital Library
- Hoa T. Dang. 2004. Investigations into the role of lexical semantics in word sense disambiguation. PhD Thesis. University of Pennsylvania. Google ScholarDigital Library
- Atsushi Fujii, Takenobu Tokunaga, Kentaro Inui, Hozumi Tanaka. 1998. Selective sampling for example-based word sense disambiguation, Computational Linguistics, v.24 n.4, p.573--597, Dec. Google ScholarDigital Library
- Eduard Hovy, Mitchell Marcus, Martha Palmer, Lance Ramshaw and Ralph Weischedel. OntoNotes: The 90% Solution. Accepted by HLT-NAACL06. Short paper. Google ScholarDigital Library
- David D. Lewis and William A. Gale. 1994. A sequential algorithm for training text classifiers. In W. Bruce Croft and Cornelis J. van Rijsbergen, editors, Proceedings of SIGIR-94, Dublin, IE. Google ScholarDigital Library
- Andrew K. McCallum. 2002. MALLET: A Machine Learning for Language Toolkit. http://www.cs.umass.edu/~mccallum/mallet.Google Scholar
- Andew McCallum and Kamal Nigam. 1998. Employing EM in pool-based active learning for text classification. In Proc. of ICML '98. Google ScholarDigital Library
- Martha Palmer, Hoa Trang Dang and Christiane Fellbaum. (to appear, 2006). Making fine-grained and coarse-grained sense distinctions, both manually and automatically. Natural Language Engineering.Google Scholar
- Andrew I. Schein. 2005. Active Learning for Logistic Regression. Ph.D. Thesis. Univ. of Pennsylvania. Google ScholarDigital Library
- Dan Shen, Jie Zhang, Jian Su, Guodong Zhou and Chew Lim Tan. 2004 Multi-criteria-based active learning for named entity recognition, In Proc. of ACL04, Barcelona, Spain. Google ScholarDigital Library
- Min Tang, Xiaoqiang Luo, and Salim Roukos. 2002. Active learning for statistical natural language parsing. In Proc. of ACL 2002. Google ScholarDigital Library
- Cynthia A. Thompson, Mary Elaine Califf, and Raymond J. Mooney. 1999. Active learning for natural language parsing and information extraction. In Proc. of ICML-99. Google ScholarDigital Library
- An empirical study of the behavior of active learning for word sense disambiguation
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