Agirre, E., & Edmonds, P. (2007). Word sense disambiguation: Algorithms and applications. Text, Speech and Language Technology Series (Vol. 33). Springer, Netherland. ISBN: 978-1-4020-6870-6.
Berger, A. L., Della Piertra, S. A., & Della Pietra, V. J. (1996). A maximum entropy approach to natural language processing. Computational Linguistics,
Bikel, D. M. (2002). Design of a multi-lingual, parallel-processing statistical parsing engine. In Proceedings of HLT 2002, San Diego, CA.
Bikel, D. M., Schwartz, R., & Weischedel, R. M. (1999). An algorithm that learns what’s in a name. Machine Learning, 34(1–3). Special Issue on Natural Language Learning.
Cai, J. F., Lee, W. S., & Teh, Y. W. (2007). NUS-ML: Improving word sense disambiguation using topic features. In Proceedings of the 4th international workshop on Semantic Evaluations (SemEval 2007), Prague, Czech Republic (pp. 249–252).
Carpuat, M., & Wu, D. (2007). Improving statistical machine translation using word sense disambiguation. In Proceedings of the 2007 joint conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) (pp. 61–72).
Chan, Y. S., Ng, H. T., & Chiang, D. (2007, June). Word sense disambiguation improves statistical machine translation. In Proceedings of the 45th annual meeting of the association of computational linguistics, Prague, Czech Republic. Association for Computational Linguistics (pp. 33–40).
Chen, J. (2006). Towards high-performance word sense disambiguation by combining rich linguistic knowledge and machine learning approaches. PhD Thesis, University of Pennsylvania.
Chen, J., Dligach, D., & Palmer, M. (2007). Towards large-scale high-performance English verb sense disambiguation by using linguistically motivated features. In Proceedings of the international conference on semantic computing (ICSC 2007). Irvine, CA.
Chen, J., & Palmer, M. (2005, October 11–13). Towards robust high performance word sense disambiguation of English verbs using rich linguistic features. In Proceedings of the 2nd international joint conference on natural language processing, Jeju Island, Korea.
Chen, S. F., & Rosenfeld, R. (1999). A Gaussian prior for smoothing maximum entropy models. Technical Report CMU-CS-99-108, CMU.
Chen, J., Schein, A., Ungar, L., & Palmer, M. (2006). An empirical study of the behavior of word sense disambiguation. In Proceedings of NAACL-HLT 2006, NY, 2006.
Dang, H. T. (2004). Investigations into the role of lexical semantics in word sense disambiguation. PhD Thesis, University of Pennsylvania.
Dang, H. T., & Palmer, M. (2005, June 26–28). The role of semantic roles in disambiguating verb senses. In Proceedings of the 43rd annual meeting of the association for computational linguistics, Ann Arbor, MI.
Dang, H. T., & Palmer, M. (2002). Combining contextual features for word sense disambiguation. In Proceedings of the SIGLEX/SENSEVAL workshop on WSD: Recent successes and future directions, in conjunction with ACL-02, Philadelphia.
Duffield, C. J., Hwang, J. D., Brown, S. W., Dligach, D., Vieweg, S. E., Davis, J., & Palmer, M. (2007). Criteria for the manual grouping of verb senses. In Linguistics annotation workshop, held in conjunction with ACL-2007, Prague, The Czech Republic.
Edmonds, P., & Cotton, S. (2001). SENSEVAL-2: Overview. In Proceedings of SENSEVAL-2: 2nd international workshop on evaluating WSD systems. ACL-SIGLEX, Toulouse France.
Fellbaum, C. (1998). WordNet—an electronic lexical database. Cambridge, MA/London: The MIT Press.
Fellbaum, C., Delfs, L., Wolff, S., & Palmer, M. (2005). Word meaning in Dictionaries, corpora, and the speaker’s mind. In G. Barnbrook, P. Danielsson, & M. Mahlberg (Eds.), Meaningful texts: The extraction of semantic information from monolingual and multilingual corpora (pp. 31–38). Birmingham, UK: Birmingham University Press.
Fellbaum, C., Palmer, M., Dang, H. T., Delfs, L., & Wolf, S. (2001, June 2, 3). Manual and automatic semantic annotation with WordNet. In SIGLEX workshop on WordNet and other lexical resources (NAACL-01), Invited talk, Pittsburgh, PA.
Gonzalo, J., Verdejo, F., Chugur, I., & Cigarran, J. (1998). Indexing with WordNet synsets can improve text retrieval. In Proceedings of the COLING/ACL’98 workshop on usage of WordNet for NLP, Montreal, Canada.
Hanks, P. (1996). Contextual dependencies and lexical sets. The International Journal of Corpus Linguistics, 1, 1.
Hovy, E., Marcus, M., Palmer, M., Ramshaw, L., & Weischedel, R. (2006). OntoNotes: The 90% solution. In Proceedings of HLT-NAACL06, New York.
Ide, N., & Veronis, J. (1998). Introduction to the special issue on word sense disambiguation: The state of the art. Computational Linguistics, 24(1), 140.
Kipper, K., Korhonen, A., Ryant, N., & Palmer, M. (2006). Extensive classifications of English verbs. In Proceedings of the 12th EURALEX international congress, Turin, Italy.
Lappin, S., & Leass, H. (1994). An algorithm for pronominal anaphora resolution. Computational Linguistics,
Lee, Y. K., & Ng, H. T. (2002). An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation. In Proceedings of the conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 41–48).
Lee, Y. K., Ng, H. T., & Chia, T. K. (2004). Supervised word sense disambiguation with support vector machines and multiple knowledge sources. In Proceedings of SENSEVAL-3: Third international workshop on the evaluation of systems for the semantic analysis of text, Barcelona, Spain (pp. 137–140).
Levin, B. (1993). English verb classes and alternations: A preliminary investigation. Chicago: University of Chicago Press.
Lucke, J. F., & Embretson, S. (1984). The biases and mean squared errors of estimators of multinormal squared multiple correlation. Journal of Educational Statistics,
(3), 183–192. doi:10.2307/1165005
Marcus, M., Kim, G., Marcinkiewicz, M. A., MacIntyre, R., Ferguson, M., Katz, K., et al. (1994). The Penn Treebank: Annotating predicate argument structure. In Proceedings of the ARPA’94 HLT workshop.
McCallum, A. K. (2002). MALLET: A machine learning for language toolkit
Mihalcea, R., Chklovski, T., & Kilgarriff, A. (2004, July). The Senseval-3 English lexical sample task. In Proceedings of Senseval-3: The third international workshop on the evaluation of systems for the semantic analysis of text, Barcelona, Spain.
Navigli, R. (2006, July 17–18). Meaningful clustering of senses helps boost word sense disambiguation performance. In Proceedings of the 21st international conference on computational linguistics and the 44th annual meeting of the ACL, Sydney, Australia (pp. 105–112).
Navigli, R., Litkowski, K. C., & Hargraves, O. (2007, June). SemEval-2007 Task 07: Coarse-grained English all-words task. In Proceedings of SemEval, held in conjunction with ACL 2007, Prague, Czech Republic.
Palmer, M., Babko-Malaya, O., & Dang, H. T. (2004). Different sense granularities for different applications. In Proceedings of the 2nd workshop on scalable natural language understanding systems (HLT/NAACL 2004). Boston, MA.
Palmer, M., Dang, H., & Fellbaum, C. (2007, June). Making fine-grained and coarse-grained sense distinctions, both manually and automatically. Journal of Natural Language Engineering, 13(2), 137–163.
Palmer, M., Fellbaum, C., Cotton, S., Delfs, L., & Dang, H. T. (2001, July 5–6). English tasks: All-words and verb lexical sample. In Proceedings of SENSEVAL-2: Second international workshop on evaluating word sense disambiguation systems. Toulouse, France.
Palmer, M., Gildea, D., & Kingsbury, P. (2005). The proposition bank: A corpus annotated with semantic roles. Computational Linguistics,
, 1. doi:10.1162/0891201053630264
Philpot, A., Hovy, E., & Pantel, P. (2005). The omega ontology. In Proceedings of the ONTOLEX workshop at the International Conference on Natural Language Processing (IJCNLP05). Jeju Island, Korea.
Pradhan, S., Loper, E., Dligach, D., & Palmer, M. (2007, June). SemEval-2007 task-17: English lexical sample, SRL and all words. In Proceedings of SemEval, held in conjunction with ACL 2007, Prague, Czech Republic.
Ratnaparkhi, A. (1998). Maximum entropy models for natural language ambiguity resolution. Ph.D. Thesis, University of Pennsylvania.
Sanderson, M. (1994). Word sense disambiguation and information retrieval. In Proceedings of the 17th International ACM SIGIR, Dublin, Ireland
Sanderson, M. (2000). Retrieving with good sense. Information Retrieval,
Snyder, B., & Palmer, M. (2004, July). The English all-words task. In Proceedings of Senseval-3: The third international workshop on the evaluation of systems for the semantic analysis of text. Barcelona, Spain.
Stokoe, C., Oakes, M. P., & Tait, J. (2003). Word sense disambiguation and information retrieval revisited. In Proceedings of the 26th annual international ACM SIGIR conference on research and development in information retrieval, Toronto, Canada.
Yarowsky, D. (1993). One sense per collocation. In Proceedings of the 5th DARPA speech and natural language workshop.
Yarowsky, D., Cucerzan, S., Florian, R., Schafer, C., & Wicentowski, R. (2001). The Johns Hopkins SENSEVAL2 system description. In Proceedings of SENSEVAL-2: 2nd international workshop on evaluating WSD systems, Toulouse France.
Yarowsky, D., & Florian, R. (2002). Evaluating sense disambiguation across diverse parameter spaces. Journal of Natural Language Engineering,
Yi, S.-t., Loper, E., & Palmer, M. (2007, April). Can semantic roles generalize across genres? In Proceedings of NAACL 2007, Rochester, NY.
Zhong, Z., Tou Ng, H., & Chan, Y. S. (2008, October). Word sense disambiguation using OntoNotes: An empirical study. In Proceedings of EMNLP 2008, Waikiki, Honolulu, HI.