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Latent semantic analysis information


Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close in meaning will occur in similar pieces of text (the distributional hypothesis). A matrix containing word counts per document (rows represent unique words and columns represent each document) is constructed from a large piece of text and a mathematical technique called singular value decomposition (SVD) is used to reduce the number of rows while preserving the similarity structure among columns. Documents are then compared by cosine similarity between any two columns. Values close to 1 represent very similar documents while values close to 0 represent very dissimilar documents.[1]

An information retrieval technique using latent semantic structure was patented in 1988 (US Patent 4,839,853, now expired) by Scott Deerwester, Susan Dumais, George Furnas, Richard Harshman, Thomas Landauer, Karen Lochbaum and Lynn Streeter. In the context of its application to information retrieval, it is sometimes called latent semantic indexing (LSI).[2]

  1. ^ Susan T. Dumais (2005). "Latent Semantic Analysis". Annual Review of Information Science and Technology. 38: 188–230. doi:10.1002/aris.1440380105.
  2. ^ "The Latent Semantic Indexing home page".

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Latent semantic analysis

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Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between...

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Probabilistic latent semantic analysis

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Probabilistic latent semantic analysis (PLSA), also known as probabilistic latent semantic indexing (PLSI, especially in information retrieval circles)...

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Semantic memory

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experiment. The two measures used to measure semantic relatedness in this model are latent semantic analysis (LSA) and word association spaces (WAS). The...

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Latent and observable variables

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analysis Partial least squares regression Latent semantic analysis and probabilistic latent semantic analysis EM algorithms Metropolis–Hastings algorithm...

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Latent space

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networks. Induced topology Clustering algorithm Intrinsic dimension Latent semantic analysis Manifold hypothesis Nonlinear dimensionality reduction Self-organizing...

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Latent class model

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p_{it}\,p_{jt}.} This two-way model is related to probabilistic latent semantic analysis and non-negative matrix factorization. The probability model used...

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Latent semantic mapping

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implementing latent semantic mapping. Latent semantic analysis API Reference: Latent Semantic Mapping Framework Reference Bellegarda, J.R. (2005). "Latent semantic...

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Latent semantic structure indexing

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Latent semantic structure indexing (LaSSI) is a technique for calculating chemical similarity derived from latent semantic analysis (LSA). LaSSI was developed...

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Explicit semantic analysis

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are equated with concepts. The name "explicit semantic analysis" contrasts with latent semantic analysis (LSA), because the use of a knowledge base makes...

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Word2vec

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algorithms[further explanation needed] such as those using n-grams and latent semantic analysis. By 2022, the straight Word2vec approach was described as "dated...

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Distributional semantics

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including latent semantic analysis (LSA), Hyperspace Analogue to Language (HAL), syntax- or dependency-based models, random indexing, semantic folding and...

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Semantic similarity

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statistical model of documents, and use it to estimate similarity. LSA (latent semantic analysis): (+) vector-based, adds vectors to measure multi-word terms; (−)...

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Semantic space

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lot of attention around the general idea of creating semantic spaces: latent semantic analysis and Hyperspace Analogue to Language. However, their adoption...

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Sentiment analysis

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such as latent semantic analysis, support vector machines, "bag of words", "Pointwise Mutual Information" for Semantic Orientation, semantic space models...

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Thomas Landauer

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one of the pioneers of Latent semantic analysis. His publications include: The Trouble with Computers, a controversial analysis of the productivity paradox...

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Latent Dirichlet allocation

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algorithm. LDA is a generalization of older approach of probabilistic latent semantic analysis (pLSA), The pLSA model is equivalent to LDA under a uniform Dirichlet...

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Gensim

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doc2vec algorithms, as well as latent semantic analysis (LSA, LSI, SVD), non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), tf-idf...

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Scott Deerwester

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Scott Deerwester (born 1956) is one of the inventors of latent semantic analysis. He was a member of the faculty of the Colgate University, University...

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Quantum cognition

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Aerts and Czachor identified quantum structure in semantic space theories, such as latent semantic analysis. Since then, the employment of techniques and...

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Topic model

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Another one, called probabilistic latent semantic analysis (PLSA), was created by Thomas Hofmann in 1999. Latent Dirichlet allocation (LDA), perhaps...

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Dimensionality reduction

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Information gain in decision trees Johnson–Lindenstrauss lemma Latent semantic analysis Local tangent space alignment Locality-sensitive hashing MinHash...

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Statistical semantics

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contribution to statistical semantics. An early success in the field was latent semantic analysis. Research in statistical semantics has resulted in a wide variety...

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George Furnas

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His early role in the analysis of the "Vocabulary Disagreement" problem lead to his co-invention of Latent Semantic Analysis for indexing and text processing...

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LSA

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referred to as NORM, naturally occurring radioactive material Latent semantic analysis, a technique in natural language processing Link-state advertisement...

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Emily Howell

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its own "personal" style. The software appears to be based on latent semantic analysis. Emily Howell's first album was released in February 2009 by Centaur...

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