Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Concept decompositions for large sparse text data using clustering
Machine Learning
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
An unsupervised vector approach to biomedical term disambiguation: integrating UMLS and Medline
HLT-SRWS '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Student Research Workshop
Acquiring sense tagged examples using relevance feedback
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Personalizing PageRank for word sense disambiguation
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Query Expansion for UMLS Metathesaurus Disambiguation Based on Automatic Corpus Extraction
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
Ontology-Based word sense disambiguation for scientific literature
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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Word sense disambiguation (WSD) is an intermediate task within information retrieval and information extraction, attempting to select the proper sense of ambiguous words. For instance, the word cold could either refer to low temperature or viral infection. Due to the scarcity of training data, knowledge-based and knowledge-lean methods receive attention as disambiguation methods. Knowledge-based methods compare the context of the ambiguous word to the information available in a terminological resource, but their main purpose is not word sense disambiguation. Knowledge-lean unsupervised methods rely on term distributions instead of a resource enumerating the possible senses but might be inappropriate when there is a requirement to commit to a terminological resource as a catalog for candidate senses. We present preliminary results of the combination of knowledge-based and knowledge-lean unsupervised methods which improves the performance of knowledge-based methods between 3% and 8%. The evaluation is done on a new word sense disambiguation set which is available to the community.