Large Scale Experiments with Naive Bayes and Decision Trees for Function Tagging

This paper describes the use of two machine learning techniques, naive Bayes and decision trees, to address the task of assigning function tags to nodes in a syntactic parse tree. Function tags are extra functional information, such as logical subject or predicate, that can be added to certain nodes in syntactic parse trees. We model the function tags assignment problem as a classification problem. Each function tag is regarded as a class and the task is to find what class/tag a given node in a parse tree belongs to from a set of predefined classes/tags. The paper offers the first systematic comparison of the two techniques, naive Bayes and decision trees, for the task of function tags assignment. The comparison is based on a standardized data set.

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