Computational and communicative efficiency in language

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Abstract/Contents

Abstract
As humans, we use language with ease and speed, solving the complex computational problem of processing form and meaning seemingly without effort. This dissertation studies how the properties of language enable us to achieve this, by investigating what is computationally difficult about language, and what is easy. We first investigate the principle of least effort, formalize it using contemporary machine learning methods, and argue that it can account for prominent typological patterns in word order. We then study the interplay of memory and surprisal in language processing, drawing on information-theoretic techniques to show that the order of words and morphemes efficiently trades off these aspects of complexity. Third, we investigate what makes language comprehension difficult for machines, proposing and validating a complexity metric that predicts the success of machine learning algorithms. Taken together, this dissertation introduces formal and computational techniques to precisely quantify the complexity of processing language, and to understand its implications for the structure of human language.

Description

Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2022; ©2022
Publication date 2022; 2022
Issuance monographic
Language English

Creators/Contributors

Author Hahn, Michael Hermann
Degree supervisor Degen, Judith
Degree supervisor Jurafsky, Dan, 1962-
Thesis advisor Degen, Judith
Thesis advisor Jurafsky, Dan, 1962-
Thesis advisor Frank, Michael C, (Professor of human biology)
Degree committee member Frank, Michael C, (Professor of human biology)
Associated with Stanford University, Department of Linguistics

Subjects

Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Michael Hermann Hahn.
Note Submitted to the Department of Linguistics.
Thesis Thesis Ph.D. Stanford University 2022.
Location https://purl.stanford.edu/np493db0254

Access conditions

Copyright
© 2022 by Michael Hermann Hahn

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