An introduction to latent semantic analysis

By T. Landauer, Peter W. Foltz, Darrell Laham. 1998

Subfields: Natural Language Processing, Representation & Self-Supervised Learning

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Citations in the corpus

On the origin of long-range correlations in texts (2012, methodology): “[1] Landauer TK, Foltz P, Laham D (1998) Introduction to latent semantic analysis.”
Discriminative Improvements to Distributional Sentence Similarity (2013, background): “…(Wan et al., 2006; Madnani et al., 2012), as well as string kernels (Bu et al., 2012); (2) syntactic operations on the parse structure (Wu, 2005; Das and Smith, 2009); and (3) distributional methods, such as latent semantic analysis (LSA; Landauer et al., 1998), which are most relevant to our work.”
Are Distributional Representations Ready for the Real World? Evaluating Word Vectors for Grounded Perceptual Meaning (2017, positive): “We deﬁne concept-concept similarity by the cosine distance between their corresponding LSA vectors.”
Language comprehension is both embodied and symbolic (2008, background): “For instance, by comparing student essays with ideal essays, LSA can automatically grade assignments (Landauer et al. 1998) and performs equally well as students on the Test of English as a Foreign Language (Landauer and Dumais 1997).”
Document-level translation quality estimation: exploring discourse and pseudo-references (2014, methodology): “Landauer et al. (1998) suggest the use of TF-IDF transformation for that.”
AutoTutor: an intelligent tutoring system with mixed-initiative dialogue (2005, background): “LSA is a high-dimensional statistical technique that measures the conceptual similarity of any two pieces of text [9], [10].”
Inferring query models by computing information flow (2002, methodology): “SVD is also used in the Latent Semantic Analysis (LSA) or more specifically to IR, Latent Semantic Indexing (LSI), to reduce the number of dimensions of a term-passage matrix [5, 8, 11].”
Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring (2017, methodology): “Following that, IntelliMetric 2, Intelligent Essay Assessor (IEA) (Landauer et al., 1998; Foltz et al., 1999) Burstein, 2004), has been deployed in the English language test, such as Test of English as a Foreign Language (TOEFL) and Graduate Record Examination (GRE).”
Evolution of Semantic Similarity—A Survey (2020, background): “…represents a given matrix as a product of three matrices, where two matrices represent the rows and columns as vectors derived from their eigenvalues and the third matrix is a diagonal matrix that has values that would reproduce the original matrix when multiplied with the other two matrices[40].”
Exploring determinants of voting for the "helpfulness" of online user reviews: A text mining approach (2011, background): “Limited by the space and scope of this paper, we will not discuss the details of SVD; more information regarding SVD can be found in [18].”
AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring (2014, background): “Around the same time that this strategy was developed, latent semantic analysis (LSA) emerged in natural language processing (Deerwester et al. 1990; Landauer et al. 1998).”
An information retrieval approach to concept location in source code (2004, methodology): “Here SVD is used as a black box subroutine, the definitions and underlying theory can be found in the relevant literature [8, 14, 30].”
Automated Essay Scoring Using Bayes' Theorem (2002, methodology): “The approach by Landauer et al. (1998) is a factor-analytic model of word cooccurrences which emphasizes essay content.”