An Assisted Literature Review using Machine Learning Models to Identify and Build a Literature Corpus
Journal Title: International Journal of Engineering and Science Invention - Year 2017, Vol 6, Issue 7
Abstract
With the evolving and interdisciplinary nature of research, there is a need to facilitate and assist researchers in the manual process of building a literature review. This paper proposes an assisted literature review prototype based on machine learning models (MLM) to discover, rank and recommend the relevant papers. Using text and data mining models, MLM and a classification model, all of which learn from researchers’ annotated data and semantic enriched metadata, assisted literature review helps researchers identify, rank and select papers. This prototype evaluates papers and bibliographic attributes in order to determine their relevancy and aggregates all relevant contents into an assisted literature review object. This paper presents the MLM and related algorithms that: 1. Identify the relevant papers harvested from the web and other sources for building the Literature Corpus; 2. Obtain the Literature Corpus radius by calculating the distance of each paper to the center of the Literature Corpus defined for a specific topic, concept or area of research. The performance, in terms of accuracy, was evaluated and compared to other approaches using a number of assisted literature review prototype simulations with a manual literature review metadata as input parameters.
Authors and Affiliations
Ronald Brisebois, Alain Abran, Apollinaire Nadembega, Philippe N‘techobo
Keywords
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