In this paper we propose an automatic term extraction approach that uses machine learning incorporating varied and rich features of candidate terms. In our preliminary experiments, we also tested different attribute selection methods to verify which features are more relevant for automatic term extraction. We achieved state of the art results for unigram extraction in Brazilian Portuguese.
More recently, REF achieved state-of-the-art performance for unigram term extraction in Brazilian Portuguese using supervised learning algorithms and a rich feature set.
A Machine Learning Approach to Automatic Term Extraction using a Rich Feature Set
NAACL
Computer Science