From short term memory to semantics - a computational model

Abstract

Aphasias are disorders of language and have been shown in recent literature, to be associated with deficits of Short Term Memory (STM). Physiological STM has a semantic component that is responsible for comprehension of language. This work brings forth a new model that learns semantics of words across an STM. The. approach to validating the model is through experiments that model word comprehension at the level of several sentences which when taken together convey semantics. Experimentally, the model when used to assign semantic labels to a collection of sentences, gives an accuracy that is comparable to or better than that obtained using Support Vector Machines (SVM).