SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
A vector space model for automatic indexing
Communications of the ACM
An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Corpus-based Techniques for Word Sense Disambiguation
Corpus-based Techniques for Word Sense Disambiguation
Using Linear Algebra for Intelligent Information Retrieval
Using Linear Algebra for Intelligent Information Retrieval
Similarity-based word sense disambiguation
Computational Linguistics - Special issue on word sense disambiguation
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Entity-based cross-document coreferencing using the Vector Space Model
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Scaling to very very large corpora for natural language disambiguation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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Word sense disambiguation is the task to identify the intended meaning of an ambiguous word in a certain context, one of the central problems in natural language processing. This paper describes four novel supervised disambiguation methods which adapt some familiar algorithms. They built on the Vector Space Model using an automatically generated stop list and two different statistical methods of finding index terms. These proceedings allow a fully automated and language independent disambiguation. The first method is based upon Latent Semantic Analysis, an automatic indexing method employed for text retrieval. The second one disambiguates via co-occurrence vectors of the target word. Disambiguation relying on Naive Bayes uses the Naive Bayes Classifier and disambiguation relying on SenseClusters1 uses an unsupervised word sense discrimination technique. These methods were implemented and evaluated to experience their performance, to compare the different approaches and to draw conclusions about the main characteristic of supervised disambiguation. The results show that the classification approach using Naive Bayes is the most efficient, scalable and successful method.