# Handbook of latent semantic analysis

@inproceedings{Landauer2007HandbookOL, title={Handbook of latent semantic analysis}, author={Thomas K. Landauer and Danielle S. McNamara and Simon J. Dennis and Walter Kintsch}, year={2007} }

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…

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## References

SHOWING 1-3 OF 3 REFERENCES

The Measurement of Textual Coherence with Latent Semantic Analysis.

- Computer Science
- 1998

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.