SIMILE is focused on developing robust, open source tools based on Semantic Web technologies that improve access, management and reuse among digital assets.
Things we do or don’t eat, mate-with, or flee-from, or the things that we describe, through our language, as prime numbers, affordances, absolute discriminables, or truths. That is all that cognition is for, and about.
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.
In this paper we propose the type of Bayesian networks that we call the hierarchical Bayesian network (HBN) classifiers. We present algorithms for the construction of the HBN classifiers and test them on the Reuters text categorization test collection
In this introduction to the special issues, we begin by outlining a concrete example that indicates some of the motivations leading to the widespread use of inheritance networks in computational linguistics.
There are now many computer programs for automatically determining the sense of a word in context (Word Sense Disambiguation or WSD). The purpose of Senseval is to evaluate the strengths and weaknesses of such programs with respect to different words, dif
The proposal Algorithms for Linguistic Processing focuses on two crucial problem areas in computational linguistics: problems of processing efficiency and ambiguity.