TEMPORAL INFERENCES IN COMPUTATIONAL LINGUISTIC INFORMATION PROCESSING (ARTIFICIAL INTELLIGENCE, PROBLEM SOLVING, DATABASE MANAGEMENT, COGNITION, KNOWLEDGE REPRESENTATION)

This dissertation aims for an integration of insights gained from linguistic, psychological and Artificial Intelligence based research to provide a pragmatic theory and mental model of how natural language processing and temporal inferences can be explained with the framework of computational information processing. A pragmatic theory focuses on the information from the context (e.g., co-text, text-type, and audience) to explain linguistic behavior. A mental model provides an internal representation of the state of affairs that are described in a given sentence.;The computer program (implemented in ELISP on EFRL on a DEC20/60) performs the following tasks: (1) parse a text from a medical journal while using linguistic and extra-linguistic knowledge; (2) map the parsed linguistic structure into an event-representation; (3) draw temporal and factual inferences within the domain of liver diseases; (4) create and update a data base containing the pertinent information about a patient.;The objectives of this research are twofold, whereby the computer program is meant to be a particular implementation of a general natural language processing system which could be used for a variety of domains. The first objective is to show how an integration of linguistic and extra-linguistic knowledge achieves a form of comprehension, where comprehension is characterized as a conversion of information based on knowledge from one representation into another. The second objective is to show that such a procedural approach is a basis for an event-based theory for temporal information processing.