Abstract: Term extraction from research articles is one of the key technologies in literature knowledge mining . The goal is to improve the efficiency of term extraction. Nowadays , term extraction can be classified into three categories , that is , rule-based method , statistical method and supervised learning method. Firstly , this p a p er carries out the comparative stud y on term extraction by experimental methods , including linguistic method , statistical method ( TF-IDF , C-value , KL dispersion-based methods , etc. ), CRF , and BiLSTM. Secondly , since lacking of massive cor p us labeling by manual , therefore , this p a p er p resents an imp roved model for term extraction task in academic literature. Finally , this article summarizes the experimental finding s and proposes the methodologies of semantic entity recognition for the current stage.

Key words: Semantic web , Research article , Term extraction , Knowledge graph , Corpus annotation, Concept learning