Language Independent System for Definition Extraction: First Results Using Learning Algorithms

In this paper we report on the performance of different learning algorithms and different sampling technique applied to a definition extraction task, using data sets in different language. We compare our results with those obtained by handcrafted rules to extract definitions. When Definition Extraction is handled with machine learning algorithms, two different issues arise. On the one hand, in most cases the data set used to extract definitions is unbalanced, and this means that it is necessary to deal with this characteristic with specific techniques. On the other hand it is possible to use the same methods to extract definitions from documents in different corpus, making the classifier language independent.

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