MACHINE LEARNING AND THE COGNITIVE BASIS OF NATURAL LANGUAGE

Shalom Lappin
King's College London

Friday, April 15, 3:30PM MJH Rm 126

Sponsored by the Stanford Humanities Center/Mellon Foundation
Graduate Research Program




The past fifteen years have seen a massive expansion in the application of information theoretic and machine learning methods to natural language processing. This work has yielded impressive results in accuracy and coverage for engineering systems addressing a wide variety of tasks in areas like speech recognition, morphological analysis, parsing, semantic interpretation, and dialogue management. It is worth considering whether the inductive learning mechanisms that these methods employ have consequences not simply for natural language engineering, but also for our understanding of the cognitive basis of human language acquisition and processing. Most machine learning has used supervised learning techniques. These have limited implications for theories of human language learning, given that they require annotation of the training data with the structures and rules that are to be learned. However, recently there has been an increasing amount of promising research on unsupervised machine learning of linguistic knowledge. The results of this research suggest the computational viability of the view that general cognitive learning and projection mechanisms rather than a richly articulated language faculty may be sufficient to support language acquistion and intepretation.











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