Predicting corpus example quality via supervised machine learning

Authors: Nikola Ljubešić, Mario Peronja

In this paper we present a supervised-learning approach to extracting good dictionary examples from corpora.We train our predictor of quality on a dataset of corpus examples annotated with a four-level ordinal variable, ranging from a very bad to a very good example. Each of the examples is formally described through 23 variables; the dependence of the quality of which is modelled using a regression model. The evaluation of the ranked results for each of the collocations in the annotated dataset shows that we obtain precision on 10 top-ranked examples of ~80% and a precision of ~90% on the three top-ranked examples. Our approach is highly language independent as well, suffering almost no loss on the 10 top-ranked examples and a loss of ~4% on the three highest-ranked examples once the language-dependent and knowledge-source-dependent features are removed.

Keywords: dictionary example; corpus extraction; supervised machine learning

Reference: In Kosem, I., Jakubiček, M., Kallas, J., Krek, S. (eds.) Electronic lexicography in the 21st century: linking lexical data in the digital age. Proceedings of the eLex 2015 conference, 11-13 August 2015, Herstmonceux Castle, United Kingdom. Ljubljana/Brighton: Trojina, Institute for Applied Slovene Studies/Lexical Computing Ltd., pp. 477-485.


Published: 2015