Identifying Expertise Tags of Scholars by Multiple Features of Academic Publications postprint

Abstract: [Purpose/significance] Identifying expertise tags of scholars is the most critical task in scholar profiling. Expertise tags contribute to finding peer experts, clustering domain scholars and selecting reviewers.[Method/process] This study analyzed related factors on the scholar expertise in academic publications, then constructed a hierarchical analysis model on the weight allocation of the factors. The TextRank algorithm has been used to identify topical terms in Chinese corpus, and the conceptual linking technique in English corpus. The extracted terms, together with the previously analyzed factors have been combined to select the expertise tags of the scholars. In this study, a group of honored scholars of different domains have been selected. Their research expertise information from their resumes have been taken as evaluation benchmark. And the expertise tags extracted from their publications have been compared with the benchmark by human judgment and additional semantic similarity judgment.[Result/conclusion] The evaluation shows that the expertise tags of 71.9% scholars are acceptable for Chinese, and 77.2% for English. The experiment proves that the method proposed in this article is pragmatic and may lead to reasonable results. The chief innovation of this study lies in three aspects, Firstly, term extraction approaches that suit to different application conditions have been explored, such as the language of publication and the availability of domain knowledge base. Secondly, multiple features have been combined together to identify the expertise tags of scholars, including the content of publications, the substantial contribution to the publications of the scholars, and the influence to the domain of the publications. Thirdly, a reasonable experimental design and evaluation method is proposed, and the proposed approach has been verified by combining manual scoring and semantic calculation results.