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- I'm currently working with a text corpus comprising 4200 articles covering a wide range of topics including Hinduism, Christianity, Islam, worldly life, Epics, mythology, and others, totaling 16 predefined labels. These articles were initially uncategorized but naturally encompass various subjects. I'm seeking machine learning models capable of automatically assigning relevant labels to each article based on its content. The objective is to tag each article with all applicable labels that accurately represent the topics it discusses.
For instance, consider an article that discusses the philosophical aspects of Hinduism, the impact of worldly life on spiritual practices, and draws parallels with mythological narratives. Such an article should ideally be tagged with labels like "Hinduism," "Worldly Life," and "Epics and Mythology" to accurately reflect its content.
- Additionally, I'm interested in exploring machine learning algorithms that can generate new labels closely aligned with the existing ones and the content within each article. This approach aims to enhance the categorization and organization of the corpus, thereby improving the efficiency of retrieval and analysis. Any help or insights into suitable models and algorithms for this task will be greatly appreciated.
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