Abstract
This paper deals with the task of definition extraction with the training corpus suffering from the problems of small size, high noise and heavy imbalance. A previous approach, based on manually constructed shallow grammars, turns out to be hard to better even by such robust classifiers as SVMs, AdaBoost and simple ensembles of classifiers. However, a linear combination of various such classifiers and manual grammars significantly improves the results of the latter.
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Przepiórkowski, A., Marcińczuk, M., Degórski, Ł. (2008). Dealing with Small, Noisy and Imbalanced Data. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2008. Lecture Notes in Computer Science(), vol 5246. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87391-4_23
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DOI: https://doi.org/10.1007/978-3-540-87391-4_23
Publisher Name: Springer, Berlin, Heidelberg
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