A Neurophysiologically-Inspired Statistical Language Model
Jonathan M. Dehdari · OhioLink ETD Center (Ohio Library and Information Network) · 2014
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 (Chen and Goodman, 1998) for large-scale textual datasets in English, Arabic, Croatian, and Hungarian.