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This paper presents a dataset and supervised learning experiments for termextraction from Slovene academic texts. Term candidates in the dataset wereextracted via morphosyntactic patterns and annotated for their termness by fourannotators. Experiments on the dataset show that most co-occurrence statistics,applied after morphosyntactic patterns and a frequency threshold, perform closeto random and that the results can be significantly improved by combining, withsupervised machine learning, all the seven statistic measures included in thedataset. On multi-word terms the model using all statistics obtains an AUC of0.736 while the best single statistic produces only AUC 0.590. Among manyadditional candidate features, only adding multi-word morphosyntactic patterninformation and length of the single-word term candidates achieves furtherimprovements of the results.

KAS-term: Extracting Slovene Terms from Doctoral Theses via Supervised Machine Learning