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.
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.
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.
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.
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.
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