Word Sense Disambiguation


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The order has two parts: one-page report to answer all the questions listed and the Java documentation.
First, make sure you understand: What is the problem of Word Sense Disambiguation (WSD)? Why is it important from a language technology engineering point of view? How is it a classification problem? How do you use SVM learning and classification to solve classification problems generally and in this WSD case? (No detailed understanding of the internal mechanisms of SVM:s and SVM learning algorithms is required.) What is an n-fold crossvalidation? How do you compute the precision, recall and F1 scores? And what do they show?
The course literature, the links given here, and other net resources give plenty of useful information about the matters important to this assignment.
A Java implementation for WSD experiments provides the point of departure for the assignment [package wsd (tar), doc].
The sense keys are from WordNet and you can see how they are explained there. Classification applies to tokens belonging to a certain lemma, defined by the lemma and pos attributes, and is binary, predicting that a token has a certain sense (defined by a WordNet sense key) or that it doesn’t have that sense (has another sense).



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