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From Utterances to Speech Acts

By Mikhail Kissine

"Kissine offers a new theory of speech acts which is philosophically sophisticated and builds on work in cognitive science, formal semantics, and linguistic typology. This highly readable, brilliant essay is a major contribution to the field."

--François Recanati, Institut Jean-Nicod

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Academic Paper


Title: Unsupervised Learning of Natural Languages
Paper URL: http://kybele.psych.cornell.edu/~edelman/abstracts.html#pnas05
Author: Shimon Edelman
Email: click here to access email
Homepage: http://kybele.psych.cornell.edu/~edelman
Institution: Cornell University
Linguistic Field: Cognitive Science; Computational Linguistics; Language Acquisition; Psycholinguistics
Abstract: We address the problem, fundamental to linguistics, bioinformatics and certain other disciplines, of using corpora of raw symbolic sequential data to infer underlying rules that govern their production. Given a corpus of strings (such as text, transcribed speech, chromosome or protein sequence data, sheet music, etc.), our unsupervised algorithm recursively distills from it hierarchically structured patterns. The ADIOS (Automatic DIstillation of Structure) algorithm relies on a statistical method for pattern extraction and on structured generalization, two processes that have been implicated in language acquisition. It has been evaluated on artificial context-free grammars with thousands of rules, on natural languages as diverse as English and Chinese, and on protein data correlating sequence with function. This is the first time an unsupervised algorithm is shown capable of learning complex syntax, generating grammatical novel sentences, and proving useful in other fields that call for structure discovery from raw data, such as bioinformatics.
Type: Individual Paper
Status: Completed
Publication Info: Proc. Natl. Acad. Sci. 102:11629-11634 (August 16, 2005)
URL: http://kybele.psych.cornell.edu/~edelman/abstracts.html#pnas05


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