Handbook of Latent Semantic Analysis

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Overview

Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the meaning of words and passages of words. Meaning is estimated using statistical computations applied to large corpora of text. As a representative sample of human language experience, text embodies the same mutual constraints that determine the semantic similarity of words and sets of words for people. A basic tenet of this approach is that human cognition has been optimized to the structure of the environment, so to understand cognition one should try to find rigorous models that match natural human achievements.

LSA has been highly successful both as a theory of verbal meaning and cognition and as a practical tool for measuring similarities and differences of meaning. Handbook of Latent Semantic Analysis provides readers with a thorough introduction to the rationale and theory underlying LSA, information about how to use LSA, implications of LSA for cognitive theories, and examples of how LSA has been applied to practical problems. Further, it illustrates how LSA can be extended to answer a wide array of theoretical and applied questions. It also discusses many of LSA's shortcomings as a complete theory of cognition, as well as philosophical questions and arguments concerning its nature. This volume will be of interest to a wide audience including philosophers of language and mind, cognitive scientists, experimental and applied psychologists, linguists, educators, and information specialists.

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Table of Contents

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. G. DenhiŠre, B. Lemaire, C. Bellissens, S. Jhean-Larose, A Semantic Space for Modeling Children's Semantic Memory. P. Foltz, Discourse Coherence and LSA. J. Quesada, Spaces for Problem Solving. Part III: LSA in Educational Applications. K. Millis, J. Magliano, K. Wiemer-Hastings, S. Todaro, D.S. McNamara, Assessing and Improving Comprehension With Latent Semantic Analysis. D.S. McNamara, C. Boonthum, I. Levinstein, K. Millis, Evaluating Self-Explanations in iSTART: Comparing Word-Based and LSA Algorithms. A. Graesser, P. Penumatsa, M. Ventura, Z. Cai, X. Hu, Using LSA in AutoTutor: Learning Through Mixed-Initiative Dialog in Natural Language. E. Kintsch, D. Caccamise, M. Franzke, N. Johnson, S. Dooley, Summary Streetr: Computer-Guided Summary Writing. L. Streeter, K. Lochbaum, N. LaVoie, J.E. Psotka, Automated Tools for Collaborative Learning Environments. Part IV: Information Retrieval and HCI Applications of LSA. S.T. Dumais, LSA and Information Retrieval: Getting Back to Basics. P.K. Foltz, T.K. Landauer, Helping People Find and Learn From Documents: Exploiting Synergies Between Human and Computer Retrieval With SuperManual. M.H. Blackmon, M. Kitajima, D.R. Mandalia, P.G. Polson, Automating Usability Evaluation Cognitive Walkthrough for the Web Puts LSA to Work on Real-World HCI Design Problems. Part V: Extensions to LSA. D.S. McNamara, Z. Cai, M.M. Louwerse, Optimizing LSA Measures of Cohesion. X. Hu, Z. Cai, P. Wiemer-Hastings, A.C. Graesser, D.S. McNamara, Strength, Weakness, and Extensions of LSA. M. Steyvers, T. Griffiths, Probabilistic Topic Models. S. Dennis, Introducing Word Order: Within the LSA Framework. Part VI: Conclusion. W. Kintsch, D.S. McNamara, S. Dennis, T.K. Landauer, LSA and Meaning: In Theory and Application.

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