LSAbased
LSA-based refers to methods that use Latent Semantic Analysis (LSA) to uncover latent semantic structure in text. LSA is a linear algebra approach that models relationships between a set of terms and a set of documents by decomposing a term-document matrix with singular value decomposition (SVD). By reducing to a lower-dimensional space, language usage patterns are captured as latent topics, enabling semantically related terms or documents to be identified even if they do not occur together.
Process typically involves preprocessing text, constructing a term-document matrix (often weighted with TF-IDF), and applying SVD
Applications include information retrieval, document clustering, semantic search, and indexing. LSA-based methods help mitigate issues of
Relation to other approaches: LSA-based techniques predate many neural methods and are deterministic, relying on linear
Advantages and limitations: LSA can improve retrieval quality and reduce noise in moderate-sized corpora, and provides