Abstract
A lot of efforts have been put in recent times for research in the field of natural language processing. Extracting topics is undoubtedly one of the most important tasks in this area of research. Latent Dirichlet allocation (LDA) is a widely used model that can perform this task in an unsupervised manner efficiently. It has been proved recently that using priors other than Dirichlet can be advantageous in extracting better quality topics from the data. Hence, in our paper we introduce the interactive latent generalized Dirichlet allocation model to extract topics. The model infers better topics using little information provided by the users through interactive learning. We use a variational algorithm for efficient inference. The model is validated against text datasets based on extracting topics related to news categories and types of emotions to test its efficiency.
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Maanicshah, K., Amayri, M., Bouguila, N. (2022). Interactive Generalized Dirichlet Mixture Allocation Model. In: Krzyzak, A., Suen, C.Y., Torsello, A., Nobile, N. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2022. Lecture Notes in Computer Science, vol 13813. Springer, Cham. https://doi.org/10.1007/978-3-031-23028-8_4
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DOI: https://doi.org/10.1007/978-3-031-23028-8_4
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