Constructing Semantic Representations From a Gradually Changing Representation of Temporal Context

Computational models of semantic memory exploit information about cooccurrences of words in naturally-occurring text to extract information about the meaning of the words that are present in the language. Such models implicitly specify a representation of temporal context. Depending on the model, words are said to have occurred in the same context if they are presented within a moving window, within the same sentence or within the same document. The temporal context model (TCM), a specific quantitative specification of temporal context has proved useful in the study of episodic memory. The predictive temporal context model (pTCM) uses the same definition of temporal context to generate semantic memory representations. Taken together pTCM and TCM may prove to be part of a general model of declarative memory.