In this paper, we perform WSD study based on large scale real-world corpus using two unsupervised learning algorithms based on ±n-improved Bayesian model and Dependency Grammar (DG)-improved Bayesian model.
Babelfy is a unified graph-based approach to multilingual Entity Linking and Word Sense Disambiguation based on a loose identification of candidate meanings coupled with a densest subgraph heuristic which selects high-coherence semantic interpretations.
This paper describes an experimental comparison between two standard supervised learning methods, namely Naive Bayes and Exemplar--based classification, on the Word Sense Disambiguation (WSD) problem.
As the use of a Bayesian probability calculation on a simple co-occurrence frequency table created from the same data has similar disambiguation capabilities, the paper also incorporates comparison of LSA with the Bayesian model.
Common sense - been through lots of iterations. Frame sentences, prompted sentences, questions asked, random sentences. Sentences/propositions recently given scores and normalised to single natural language representation; now one can vote on them.
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