Research paper classification based on Word2vec and community discovery - Gundogan, Esra; Kaya, Mehmet (Institute of Electrical and Electronics Engineers Inc., 2020)
With the advances in information technologies, different research areas are emerging day by day and thousands of research papers are published in these fields. Papers are not presented to users grouped according to their topics. Therefore, it is getting harder and harder for people to access research papers in their field of interest. To facilitate this search process, a paper classification system is proposed in this study to categorize papers on similar topics. A different approach from the studies presented for classification so far has been proposed. This system is a complex system created by combining Word2vec, network modeling and community discovery. With the Word2vec method, which has attracted great attention recently, paper similarities have been found, a network based on similarity rates has been created and papers have been clustered with the community discovery algorithm. As a result of the application with the proposed system, a success of 89% has been achieved. As can be seen from the results, this approach presented for an important classification problem will provide great convenience to people. It will enable fast and efficient access to papers. © 2020 IEEE.
2020 International Conference on Decision Aid Sciences and Application, DASA 2020 -- 7 November 2020 through 9 November 2020 -- Virtual, Sakheer -- 166557
Keywords: community discovery; research paper classification; Word2vec