Abstract
Topic detection is the process of identifying the underlying themes or topics present in a set of documents. It has become more critical due to the increase of information electronically available and the necessity to process and filter it. In this respect, we introduce a new approach to detecting topics called ClusART. Thus, we created a three-phase approach: a first phase during which lexical preprocessing was conducted. The second phase pays heed to the construction and the generation of vectors representing the documents carried out. In the topic detection phase, the FuzzyART algorithm is used for the training phase, and a classifier based on ParagraphVector is used for the test phase. The comparative study of our approach on the 20 Newsgroups dataset showed that our method could detect almost relevant topics.
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Manai, M., Ben Yahia, S. (2024). Efficient Topic Detection Using an Adaptive Neural Network Architecture. In: Saad, I., Rosenthal-Sabroux, C., Gargouri, F., Chakhar, S., Williams, N., Haig, E. (eds) Advances in Information Systems, Artificial Intelligence and Knowledge Management. ICIKS 2023. Lecture Notes in Business Information Processing, vol 486. Springer, Cham. https://doi.org/10.1007/978-3-031-51664-1_10
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