Abstract
In this paper, we propose an agent-based text mining algorithm to extract potential context of papers published in the WWW. A user provides the agent with keywords and assigns a threshold value for each given keyword, the agent in turn attempts to find papers that match the keywords within a defined threshold. To achieve context recognition, the algorithm mines the keywords and identifies the potential context from analysing a paper’s abstract. The mining process entails data cleaning, formatting, filtering, and identifying the candidate keywords. Subsequently, based on the strength of each keyword and the threshold value, the algorithm facilitates the identification of the paper’s potential context.
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Mahmoud, M.A., Ahmad, M.S., Yusoff, M.Z.M., Mustapha, A. (2015). Context Identification of Scientific Papers via Agent-Based Model for Text Mining (ABM-TM). In: Camacho, D., Kim, SW., Trawiński, B. (eds) New Trends in Computational Collective Intelligence. Studies in Computational Intelligence, vol 572. Springer, Cham. https://doi.org/10.1007/978-3-319-10774-5_5
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DOI: https://doi.org/10.1007/978-3-319-10774-5_5
Publisher Name: Springer, Cham
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