Supervised Learning for Robust Term Extraction

We propose a machine learning method to automatically classify the extracted ngrams from a corpus into terms and non-terms. We use 10 common statistics in previous term extraction literature as features for training. The proposed method, applicable to term recognition in multiple domains and languages, can help 1) avoid the laborious work in the post-processing (e.g. subjective threshold setting); 2) handle the skewness and demonstrate noticeable resilience to domain-shift issue of training data. Experiments are carried out on 6 corpora of multiple domains and languages, including GENIA and ACLRD-TEC(1.0) corpus as training set and four TTC subcorpora of wind energy and mobile technology in both Chinese and English as test set. Promising results are found, which indicate that this approach is capable of identifying both single word terms and multiword terms with reasonably good precision and recall.