LSA (Latent Semantic Analysis) — how does it relate to modern retrieval?
mediumAnswer
- Truncated SVD on TF-IDF document-term matrix.
- Rows → topic vectors for docs, columns for terms.
- Uses: information retrieval (query as bag-of-words → SVD-project → cosine-retrieve).
- Precursor to modern dense retrieval (sentence-transformers, dual encoders).
- Still useful as baseline; interpretable in low-resource languages where fine-tuned encoders aren't available.
Check yourself — multiple choice
- Random
- Truncated SVD on TF-IDF matrix → doc/term topic vectors; precursor to modern dense retrieval; still useful as baseline / low-resource languages
- Same as GPT
- Not real
LSA: truncated SVD on TF-IDF; precursor to modern dense retrieval.
#nlp#text#dimensionality-reduction
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