Abstract
This paper presents a dataset and supervised learning experiments for term extraction from Slovene academic texts. Term candidates in the dataset were extracted via morphosyntactic patterns and annotated for their termness by four annotators. Experiments on the dataset show that most co-occurrence statistics, applied after morphosyntactic patterns and a frequency threshold, perform close to random and that the results can be significantly improved by combining, with supervised machine learning, all the seven statistic measures included in the dataset. On multi-word terms the model using all statistics obtains an AUC of 0.736 while the best single statistic produces only AUC 0.590. Among many additional candidate features, only adding multi-word morphosyntactic pattern information and length of the single-word term candidates achieves further improvements of the results.
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Notes
- 1.
The complete KAS corpus and the KAS-Dr corpus of PhDs are available for exploring through the CLARIN.SI concordancers, http://www.clarin.si/info/concordances/.
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Acknowledgements
The work described in this paper was funded by the Slovenian Research Agency within the national basic research project “Slovene scientific texts: resources and description” (J6-7094, 2016–2019).
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Ljubešić, N., Fišer, D., Erjavec, T. (2019). KAS-term: Extracting Slovene Terms from Doctoral Theses via Supervised Machine Learning. In: Ekštein, K. (eds) Text, Speech, and Dialogue. TSD 2019. Lecture Notes in Computer Science(), vol 11697. Springer, Cham. https://doi.org/10.1007/978-3-030-27947-9_10
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