A model for auto-tagging of research papers based on keyphrase extraction methods

Tagging provides a convenient means to assign tokens of identification to research papers which facilitate recommendation, search and disposition process of research papers. This paper contributes a document centered approach for auto-tagging of research papers. The auto-tagging method mainly comprises of two processes:- classification and tag selection. The classification process involves automatic keyword extraction using Rapid Automatic Keyword Extraction (RAKE) algorithm which uses the keyword — score matrix. The generated top scored keywords are added to the train dataset dynamically, which can be used further. This add-on feature of the system is considered to be one of the main advantages of the system since adding new born phrases is time-consuming and error prone. Cosine similarity is used for classifying the research paper into corresponding domain utilizing the extracted keywords. Tag selection concentrates on the selection of proper tags for the research paper. Tagging facilitates better search facility and determines the dynamics of research areas in terms of number of publications in a domain by each author. The system generates reports for statistical analysis of research papers in each domain and expertise of each faculty in the research community.