Regularized least-squares classification for word sense disambiguation

Proceedings of the SENSEVAL@ACL 2004: 3rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text - Held in cooperation with ACL 2004

Volumn , Issue , 2004, Pages 209-212

  Popescu, Marius ^a  

^a UNIVERSITY OF BUCHAREST   (Romania)

Author keywords

[No Author keywords available]

Indexed keywords

LEAST SQUARES APPROXIMATIONS; NATURAL LANGUAGE PROCESSING SYSTEMS; SEMANTICS;

CLASSIFICATION LEARNING; LEARNING METHODS; REGULARIZED LEAST SQUARES; WORD SENSE DISAMBIGUATION;

LEARNING SYSTEMS;

EID: 84966689319     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. Eric Brill. 1995. Transformation-based error-driven learning and natural language processing: A case study in part of speech tagging. Computational Linguistics, 21(4):543–565.
2. M. Collins and N. Duffy. 2002. Convolution kernels for natural language. In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, pages 625–632, Cambridge, MA. MIT Press.
3. N. Cristianini, J. Shawe-Taylor, A. Elisseeff, and J. Kandola. 2002. On kernel-target alignment. In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, pages 367–373, Cambridge, MA. MIT Press.
4. Radu Florian and David Yarowsky. 2002. Modeling consensus: Classifier combination for word sense disambiguation. In Proceedings of EMNLP’02, pages 25–32, Philadelphia, PA, USA.
5. Yoong Lee and Hwee Ng. 2002. An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation. In Proceedings of EMNLP’02, pages 41–48, Philadelphia, PA, USA.
6. Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, and Chris Watkins. 2002. Text classification using string kernels. Journal of Machine Learning Research, 2(February):419–444.
7. Tomaso Poggio and Steve Smale. 2003. The mathematics of learning: Dealing with data. Notices of the American Mathematical Society (AMS), 50(5):537–544.
8. Martin Porter. 1980. An algorithm for suffix stripping. Program, 14(3):130–137.
9. Ryan Rifkin and Aldebaro Klautau. 2004. In defense of one-vs-all classification. Journal of Machine Learning Research, 5(January):101–141.
10. Ryan Rifkin. 2002. Everything Old Is New Again: A Fresh Look at Historical Approaches to Machine Learning. Ph.D. thesis, Massachusetts Institute of Technology.
11. David Yarowsky. 1993. One sense per collocation. In ARPA Human Language Technology Workshop, pages 266–271, Princeton, USA.