14 Oct 2016 - Timothy J. O'Donnell: Seminar
Computation and Storage in Language
A celebrated property of natural language is creativity, the ability to combine stored units to derive new expressions. This feature is found across multiple levels of linguistic structure: Phones can be combined to form novel morphemes, which can be combined to form novel words, which can be combined to form novel sentences. However, each human language is characterized by its own inventory of units at each level and its own constraints on their combination. A syllable like "derp" sounds like a better morpheme of English than a syllable like "denp." The existence of the words "warmth" and "truth" do not imply the possibility of "coolth," but "warmness," "trueness," and "coolness" are all grammatical. And speakers of English will naturally drop the phrase "on the counter" from "John made dinner on the counter", but not from "John put dinner on the counter".
How do learners acquire the inventory of units and constraints at each level of linguistic structure that are particular to their own language? I will discuss a theoretical framework designed to address this question. The approach is based on the idea that this problem can be solved by optimizing a probabilistic tradeoff between a pressure to store fewer, more reusable primitive units and a pressure to account for each linguistic expression in as few computational steps as possible. Although the idea behind this tradeoff is an old one, it has surprisingly deep and far-reaching consequences when applied to domain-specific models of linguistic computation. I will show how this approach can shed light on a number of long-standing problems in phonology, morphology, and syntax, emphasizing the interplay of linguistic assumptions and this general principle of inference.
Tim O'Donnell is currently a research scientist at MIT. In January 2017 he will be joining the Department of Linguistics at McGill University as an assistant professor. His research focuses on developing mathematical and computational models of language learning and processing. His work draws on techniques from computational linguistics, and artificial intelligence and integrates ideas from theoretical linguistics and methods from experimental psychology.
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