Please use this identifier to cite or link to this item:
https://dspace.univ-ghardaia.edu.dz/xmlui/handle/123456789/1022
Title: | Topic Modeling with Word Embeddings |
Authors: | Ahmed, ITBIRENE CHIHANI, Brahim |
Keywords: | topic modeling, topic coherence, Latent Dirichlet Allocation (LDA), Embedded Topic Model (ETM), Gaussian LDA (G-LDA), LDA2Vec. |
Issue Date: | 2021 |
Publisher: | université Ghardaia |
Abstract: | With the great development in the field of digitization, the extraction of topics through information that is in the form of unmarked texts, is not an easy matter. Therefore, we need a topic modeling technique, which is based on unsupervised algorithms. In our thesis, we clarify the concept of topic modeling and the inherent approaches, such as Latent Dirichlet Allocation (LDA), Embedded Topic Model (ETM), Gaussian LDA (G-LDA), and LDA with Word2Vec (LDA2Vec). In the experimental work, we make an empirical comparison between both LDA and ETM methods on the 20 newsgroups, in terms of runtime and topic coherence. The results are in favor of the ETM method |
URI: | https://dspace.univ-ghardaia.edu.dz/xmlui/handle/123456789/1022 |
Appears in Collections: | Mémoires de Master |
Files in This Item:
File | Description | Size | Format | |
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Topic_Modeling_with_Word_Embeddings.pdf | 2.93 MB | Adobe PDF | View/Open |
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