A framework for automatic construction of search profiles based on semantic topic modeling
DOI:
https://doi.org/10.5753/reic.2022.2687Keywords:
Topic modeling, Word embeddingAbstract
Recent efforts have focused on identifying multidisciplinary teams and detecting co-Authorship Networks based on exploring topic modeling to identify researchers' expertise. Though promising, none of these efforts perform a real-life evaluation of the quality of the built topics. This paper proposes a framework that allows summarizing articles written by researchers to automatically build research profiles and perform online evaluations. We perform a set of experiments, considering the Lattes repository, contrasting two types of evaluation: (1) an offline in which we exploit a traditional metric (NPMI); and (2) an online where researchers evaluate their own built profiles. We observed that using both together is very important for a comprehensive quality evaluation.
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Copyright (c) 2022 Eletronic Journal of Undergraduate Research on Computing
This work is licensed under a Creative Commons Attribution 4.0 International License.
