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

## TL;DR

This paper introduces a dataset and supervised machine learning approach for extracting Slovene academic terms from doctoral theses, demonstrating significant improvements over basic statistical methods.

## Contribution

It provides a novel annotated dataset and shows that combining multiple statistical features with supervised learning enhances term extraction accuracy.

## Key findings

- Supervised learning with combined features outperforms individual statistics.
- Multi-word term extraction achieves an AUC of 0.736.
- Adding morphosyntactic and length features improves results.

## 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.