A Neurophysiologically-Inspired Statistical Language Model

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 localand long-distance grammatical processing, as well as localand 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.