Handbook of latent semantic analysis

  title={Handbook of latent semantic analysis},
  author={Thomas K. Landauer and Danielle S. McNamara and Simon J. Dennis and Walter Kintsch},
Contents: Part I: Introduction to LSA: Theory and Methods. T.K. Landauer, LSA as a Theory of Meaning. D. Martin, M. Berry, Mathematical Foundations Behind Latent Semantic Analysis. S. Dennis, How to Use the LSA Website. J. Quesada, Creating Your Own LSA Spaces. Part II: LSA in Cognitive Theory. W. Kintsch, Meaning in Context. M. Louwerse, Symbolic or Embodied Representations: A Case for Symbol Interdependency. M.W. Howard, K. Addis, B. Jing, M.K. Kahana, Semantic Structure and Episodic Memory… 
Latent semantic analysis.
This article reviews latent semantic analysis (LSA), a theory of meaning as well as a method for extracting that meaning from passages of text, based on statistical computations over a collection of
Latent Semantic Analysis: five methodological recommendations
Five methodological issues that need to be addressed by the researcher who will embark on Latent Semantic Analysis are reviewed, involving the analysis of abstracts for papers published in the European Journal of Information Systems.
Modeling Semantic Memory
Computational models of semantics infer semantic structure from the analysis of large linguistic corpora using dimension reduction to construct a high-dimensional semantic space from such a matrix.
The construction of meaning: the role of context in corpus-based approaches to language modeling
It is shown how text is structurally decomposed and combined with the comprehenders’ prior knowledge in order to understand the text and demonstrates how the expressiveness from explicitly modeling context leads to a better word sense disambiguation process.
Principal Semantic Components of Language and the Measurement of Meaning
A low-dimensional, context-independent semantic map of natural language that represents simultaneously synonymy and antonymy is constructed, and provides a foundational metric system for the quantitative analysis of word meaning.
Bridging the theoretical gap between semantic representation models without the pressure of a ranking: some lessons learnt from LSA
A critical review of latent semantic analysis (LSA) to clarify some of the misunderstandings regarding LSA and other space models and proposes using long LSA experiences in other models, especially in predicting models such as word2vec.
A comparison between latent semantic analysis and correspondence analysis
Latent Semantic Analysis (LSA) is a technique for analyzing textual data through a singular value decomposition of term-document matrices that allows to reduce the dimensionality from several thousands to several hundred of a huge but sparse data matrix.
LSAfun - An R package for computations based on Latent Semantic Analysis
The R package LSAfun enables a variety of functions and computations based on Vector Semantic Models such as Latent Semantic Analysis (LSA), which are procedures to obtain a high-dimensional vector representation for words (and documents) from a text corpus.
Vector-Space Models of Semantic Representation From a Cognitive Perspective: A Discussion of Common Misconceptions
This article identifies common misconceptions that arise as a result of incomplete descriptions, outdated arguments, and unclear distinctions between theory and implementation of the models of semantic representation and clarify and amend these points to provide a theoretical basis for future research and discussions on vector models of semantics representation.
From Frequency to Meaning: Vector Space Models of Semantics
The goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs, and to provide pointers into the literature for those who are less familiar with the field.


The Measurement of Textual Coherence with Latent Semantic Analysis.
The approach for predicting coherence through reanalyzing sets of texts from 2 studies that manipulated the coherence of texts and assessed readers’ comprehension indicates that the method is able to predict the effect of text coherence on comprehension and is more effective than simple term‐term overlap measures.
The latent semantic analysis theory of knowledge