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A Neurophysiologically-Inspired Statistical Language Model

Dehdari, Jonathan

Abstract Details

2014, Doctor of Philosophy, Ohio State University, Linguistics.
We describe a statistical language model having components that are inspired by electrophysiological activities in the brain. These components correspond to important language-relevant event-related potentials measured using electroencephalography. We relate neural signals involved in local- and long-distance grammatical processing, as well as local- and long-distance lexical processing to statistical language models that are scalable, cross-linguistic, and incremental. We develop a novel language model component that unifies n-gram, skip, and trigger language models into a generalized model inspired by the long-distance lexical event-related potential (N400). We evaluate this model in textual and speech recognition experiments, showing consistent improvements over 4-gram modified Kneser-Ney language models for large-scale textual datasets in English, Arabic, Croatian, and Hungarian.
William Schuler (Advisor)
Eric Fosler-Lussier (Committee Member)
Per Sederberg (Committee Member)
166 p.

Recommended Citations

Citations

  • Dehdari, J. (2014). A Neurophysiologically-Inspired Statistical Language Model [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1399071363

    APA Style (7th edition)

  • Dehdari, Jonathan. A Neurophysiologically-Inspired Statistical Language Model. 2014. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1399071363.

    MLA Style (8th edition)

  • Dehdari, Jonathan. "A Neurophysiologically-Inspired Statistical Language Model." Doctoral dissertation, Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1399071363

    Chicago Manual of Style (17th edition)