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#ml #учеба
Topic modeling - unsupervised, provides set of topics of docs. In some way modification of TF-IDF to deal with synonyms and polysemy. Important to use Stemming and Lemmatization to reduce number of unique words in dictionary of documents.
LSA (latent semantic analysis or latent semantic indexing) Has to more matrixes Term x Term - number of documents, there terms co-ocur. Document x Document - number of terms each doc has in common Document x term - number of terms in each doc Use SVD (singular value decomposition) on term x doc Reduce dimensions and use cosine similarity of this resulting matrixes to measure resemblance of documents.
LDA (Latent Dirichlet allocation) probabilistic algorithm. Using doc x term matrixes (freq and co-ocurence) - generate table of probabilities for each keyword in each doc (topic distribution). While assigning topic to words it uses Gibbs sampling
sources: https://maartengr.github.io/BERTopic/getting_started/ctfidf/ctfidf.html - bertopic lib https://huggingface.co/blog/bobxwu/fastopic