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.
S. Pradhan, E. Loper, D. Dligach, and M. Palmer. Proceedings of the 4th International Workshop on Semantic Evaluations, page 87--92. Stroudsburg, PA, USA, Association for Computational Linguistics, (2007)
R. Koeling, D. McCarthy, and J. Carroll. Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, page 419--426. Stroudsburg, PA, USA, Association for Computational Linguistics, (2005)
D. Martinez, and E. Agirre. Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13, page 207--215. Stroudsburg, PA, USA, Association for Computational Linguistics, (2000)
R. Navigli, K. Litkowski, and O. Hargraves. Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007), page 30--35. Prague, Czech Republic, Association for Computational Linguistics, (June 2007)
E. Voorhees. Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval, page 171--180. New York, NY, USA, ACM, (1993)
M. Carpuat, and D. Wu. Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, page 387--394. Stroudsburg, PA, USA, Association for Computational Linguistics, (2005)
M. Sanderson. Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, page 142--151. New York, NY, USA, Springer-Verlag New York, Inc., (1994)
P. Pantel, and D. Lin. Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, page 613--619. New York, NY, USA, ACM, (2002)
B. Snyder, and M. Palmer. Senseval-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, page 41--43. Barcelona, Spain, Association for Computational Linguistics, (July 2004)