Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
HLT '93 Proceedings of the workshop on Human Language Technology
Finding predominant word senses in untagged text
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Domain-specific sense distributions and predominant sense acquisition
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Unsupervised Graph-basedWord Sense Disambiguation Using Measures of Word Semantic Similarity
ICSC '07 Proceedings of the International Conference on Semantic Computing
Unsupervised acquisition of predominant word senses
Computational Linguistics
Comparing corpora using frequency profiling
CompareCorpora '00 Proceedings of the Workshop on Comparing Corpora
Personalizing PageRank for word sense disambiguation
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
A graph-based approach to WSD using relevant semantic trees and n-cliques model
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Joining forces pays off: multilingual joint word sense disambiguation
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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We describe two systems that participated in SemEval-2010 task 17 (All-words Word Sense Disambiguation on a Specific Domain) and were ranked in the third and fourth positions in the formal evaluation. Domain adaptation techniques using the background documents released in the task were used to assign ranking scores to the words and their senses. The test data was disambiguated using the Personalized PageRank algorithm which was applied to a graph constructed from the whole of WordNet in which nodes are initialized with ranking scores of words and their senses. In the competition, our systems achieved comparable accuracy of 53.4 and 52.2, which outperforms the most frequent sense baseline (50.5).