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