Applying transductive learning for automatic term extraction: The case of the ecology domain

Abstract

Terms are the basis for general text mining and natural language processing applications. However, the manual term extraction is unfeasible due to the huge number of words presented in a domain corpus and also the human effort required to do the extraction. For the term extraction task, machine learning techniques have been used to perform automatic term extraction (ATE). Inductive learning is commonly used, but it requires a large number of words labeled as terms and non-terms to build a classification model and classify unseen words. A better solution is the use of transductive learning, since it requires a small number of labeled examples to classify words as terms and non-terms. In this paper, we propose the use of transductive learning to ATE. The obtained results demonstrate that the application of transductive learning to ATE produces better results than the results obtained by inductive learning.