Lisa S. Pearl

picture of Lisa S. Pearl

Professor, Linguistics
School of Social Sciences
Professor, Cognitive Sciences
School of Social Sciences
Chair, Linguistics
School of Social Sciences

A.B., University of Maryland, College Park, 2002, Linguistics
B.S., University of Maryland, College Park, 2002, Computer Science
Ph.D., University of Maryland, College Park, 2007, Linguistics

Fax: (949) 824-2307
Email: lpearl@uci.edu

University of California, Irvine
SSPB 2219
SBSG 2314
Irvine, CA 92697
Research Interests
Language Development, Computational & Mathematical Modeling, Natural Language Processing, Computational Sociolinguistics
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Research Abstract
My research lies primarily at the interface of language acquisition, computation, and information extraction.

Language acquisition can be thought of as the extraction of information about language from language, and much of my work concentrates on how children might accomplish this in a variety of domains. The main research technique I use is computational models of human language learning, which allows me to explore the mechanism of language acquisition given the boundary conditions provided by linguistic representation, the trajectory of acquisition, and human processing limitations.

I have also worked on making explicit, empirically grounded “arguments from acquisition” for theoretical representations. This approach capitalizes on the natural link between theories of knowledge representation and theories of knowledge acquisition. The basic idea is to evaluate a theory of representation by how useful it is for acquisition. Computational models of the acquisition process are an effective tool for determining this, since they allow us to incorporate the assumptions of a representation into a cognitively plausible learning scenario and see what happens. We can then identify which representations work for acquisition, and what those representations need to work.

A third line of research is more oriented towards natural language processing, exploring the utility of linguistically-informed statistical techniques to solve applied linguistic problems in information extraction. One branch of this research involves investigating the transmission of mental state information (such as moods, emotions, attitudes, and intentions) through language text, with the goal of building both cognitive models of information transmission in humans and machine learning algorithms capable of human-level performance.

A second branch of this more applied research involves the identification of a document’s author, using linguistic cues to that author’s style that are sometimes called an author’s “writeprint”. This is useful in situations of authorship identification (where a document may be anonymous) as well as authorship deception (where a document may have the signature of one author but really be by a different author).
Last updated
10/16/2018