An introduction to latent semantic analysis

@article{Landauer1998AnIT,
  title={An introduction to latent semantic analysis},
  author={Thomas K. Landauer and Peter W. Foltz and Darrell Laham},
  journal={Discourse Processes},
  year={1998},
  volume={25},
  pages={259-284}
}
Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual‐usage meaning of words by statistical computations applied to a large corpus of text (Landauer & Dumais, 1997). The underlying idea is that the aggregate of all the word contexts in which a given word does and does not appear provides a set of mutual constraints that largely determines the similarity of meaning of words and sets of words to each other. The adequacy of LSA's reflection of human… 

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Latent semantic analysis (LSA) is a theory of how word meaning—and possibly other knowledge—is derived from statistics of experience, and of how passage meaning is represented by combinations of

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In a recent article in Discourse Processes (Vol. 25, Nos. 2 & 3, 1998), Landauer, Foltz, and Laham described a computational model called Latent Semantic Analysis (LSA) and summarized its successful

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.

Meaning and context: the implications of LSA (latent semantic analysis) for semantics.

Until recently, cognitive science understood semantics in terms of its using the information contained in first-order (or direct) associations. In Psychological Review (1997), Landauer and Dumais in

High-Dimensional Semantic Space Accounts of Priming.

High-dimensional semantic space accounts of priming q

The computational mechanisms required to learn distributed semantic representations for words directly from unsupervised experience with language are examined, and both word context and word order information are found to be necessary to account for trends in the human data.

Chapter 4 Latent Semantic Analysis 4.1. Latent Semantic Analysis

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Latent Semantic Analysis Approaches to Categorization

Latent Semantic Analysis creates high dimensional vectors for concepts in semantic memory through statistical analysis of a large representative corpus of text rather than subjective feature sets linked to object names, and multivariate analyses of similarity matrices show more cohesive structure for natural kinds than for artifacts.

An Introduction to Latent Semantic Analysis

The Latent Semantic Analysis model (Landauer & Dumais, 1997) is a theory for how meaning representations might be learned from encountering large samples of language without explicit directions as to

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    Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society
  • 2019
This work casts the passages of a large and representative text corpus as a system of simultaneous linear equations in which passage meaning equals the sum of word meanings, and produces a high-dimensional vector representing the average contribution to passage meanings of every word.
...

References

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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.

Latent Semantic Analysis Approaches to Categorization

Latent Semantic Analysis creates high dimensional vectors for concepts in semantic memory through statistical analysis of a large representative corpus of text rather than subjective feature sets linked to object names, and multivariate analyses of similarity matrices show more cohesive structure for natural kinds than for artifacts.

A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge.

A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena.

Explorations in context space: Words, sentences, discourse

A computational model of high‐dimensional context space, the Hyperspace Analog to Language (HAL), is described and simulation evidence that HAL's vector representations can provide sufficient information to make semantic, grammatical, and abstract distinctions is presented.

How Well Can Passage Meaning be Derived without Using Word Order? A Comparison of Latent Semantic Analysis and Humans

An exploratory approach was provided by asking humans to judge the quality and quantity of knowledge conveyed by short student essays on scientific topics and comparing the interrater reliability and predictive accuracy of their estimates with the performance of a corpus-based statistical model that takes no account of word order within an essay.

Using latent semantic analysis to assess knowledge: Some technical considerations

In another article (Wolfe et al., 1998/this issue) we showed how Latent Semantic Analysis (LSA) can be used to assess student knowledge—how essays can be graded by LSA and how LSA can match students

Learning from text: Matching readers and texts by latent semantic analysis

Results show a nonmonotonic relation in which learning was greatest for texts that were neither too easy nor too difficult, and LSA proved as effective at predicting learning from these texts as traditional knowledge assessment measures.

Latent semantic analysis for text-based research

This paper summarizes three experiments that illustrate how LSA may be used in text-based research by describing methods for analyzing a subject’s essay for determining from what text a subject learned the information and for grading the quality of information cited in the essay.

The role of knowledge in discourse comprehension: a construction-integration model.

Publisher Summary This chapter discusses data concerning the time course of word identification in a discourse context. A simulation of arithmetic word-problem understanding provides a plausible

Time course of priming for associate and inference words in a discourse context

The construction of word meanings in a discourse context was conceptualized as a process of sense activation, sense selection, and sense elaboration and the results are interpreted as consistent with a model of lexical processing in which sense activation functions independently of context.
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