Word Sense Disambiguation
In addition to working on Spectral Methods for
Modeling Language, and information extraction and
text mining, for marketing, bibliometrics
and bioinformatics,
my group has worked on word sense disambiguaation and
related problems.
For more information
- Improving Supervised Sense Disambiguation with Web-scale Selectors.
H. Andrew Schwartz, Fernando Gomez, Lyle H. Ungar
COLing-2012: the 24th International Conference on Computational
Linguistics 2012.
- New Insights from Coarse Word Sense Disambiguation in the Crowd.
Adam Kapelner, Krishna Kaliannan, H. Andrew Schwartz, Lyle Ungar
and Dean Foster.
COLing-2012: the 24th International Conference on Computational
Linguistics. 2012
- Using Word Similarities to better Estimate Sentence Similarity.
Sneha Jha, H. Andrew Schwartz and Lyle H. Ungar,
Semeval 2012.
- A new approach to lexical disambiguation of Arabic text.
R. Shah, P. Dhillon, M. Liberman, D. Foster, M. Maamouri and L. Ungar,
Empirical Methods in Natural Language Processing (EMNLP) ,
725--735, 2010.
- Transfer Learning, Feature Selection and Word Sense
Disambiguation.
Paramveer Dhillon, and Lyle Ungar.
ACL-IJCNLP (Annual Meeting of the Association of Computational
Linguistics),257-260, 2009.
- An Empirical Study of the Behavior of Active Learning for Word
Sense Disambiguation.
J. Chen, A. Schein, L. Ungar and M. Palmer
HLT-NAACL 06 2006.
home: ungar@cis.upenn.edu