Computational Methods to Extract Meaning From Text and Advance Theories of Human Cognition

Topics in Cognitive Science 3 (1):3-17 (2011)
Abstract
Over the past two decades, researchers have made great advances in the area of computational methods for extracting meaning from text. This research has to a large extent been spurred by the development of latent semantic analysis (LSA), a method for extracting and representing the meaning of words using statistical computations applied to large corpora of text. Since the advent of LSA, researchers have developed and tested alternative statistical methods designed to detect and analyze meaning in text corpora. This research exemplifies how statistical models of semantics play an important role in our understanding of cognition and contribute to the field of cognitive science. Importantly, these models afford large-scale representations of human knowledge and allow researchers to explore various questions regarding knowledge, discourse processing, text comprehension, and language. This topic includes the latest progress by the leading researchers in the endeavor to go beyond LSA
Keywords LSA  Latent representations  Computational techniques  Sematic models  Memory  Cognition  Meaning extraction  Embodiment
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References found in this work BETA
Lawrence W. Barsalou (1999). Perceptual Symbol Systems. Behavioral and Brain Sciences 22 (4):577-660.
Arthur M. Glenberg (1997). What Memory is For. Behavioral and Brain Sciences 20 (1):1-19.

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