Abstract
Keyword extraction has gained increasing interest in the era of information explosion. The use of keyword extraction in documents context categorization, indexing and classification has led to the emphasis on graph-based keyword extraction. This research attempts to examine the impact of several factors on the result of using graph-based keyword extraction approach on a scientific dataset. This study applies a new model that processes the Medline scientific abstracts, produces graphs and extracts 3-graphlets and 4-graphlets from those graphs. The focus of the experiment is to come up with a dataset that consists of the keywords and their occurrences in the proposed graphlets patterns for each abstract with its class. Then, apply a supervised Naïve Bayes classifier in order to assign a probability to each word, whether or not it is a keyword, and finally evaluate the performance of the graph-based keyword extraction approach. The model achieved significant results compared to the Term Frequency/Inverse Document Frequency (TF/IDF) baseline standard. The experimental results proved the capability of using graphs and graphlet patterns in keyword extraction tasks.
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Alqaryouti, O., Khwileh, H., Farouk, T., Nabhan, A., Shaalan, K. (2018). Graph-Based Keyword Extraction. In: Shaalan, K., Hassanien, A., Tolba, F. (eds) Intelligent Natural Language Processing: Trends and Applications. Studies in Computational Intelligence, vol 740. Springer, Cham. https://doi.org/10.1007/978-3-319-67056-0_9
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DOI: https://doi.org/10.1007/978-3-319-67056-0_9
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