Natural Language Processing and Ihe Automatic Acquisition of Knowledge: A Simulative Approach

NATURAL LANGUAGE PROCESSING AND THE AUTOMATIC ACQUISITION OF KNOWLEDGE: A SIMULATIVE APPROACH Danilo FUM Laboratorio di Psicologia E.E. - Universit8 di Trieste via Tigor 22, I - 34124 Trieste (Italy) ABSTRACT The paper presents the general design and the first results of a research project whose long term goal is to develop and implement ALICE, an experimental system capable of augmenting its knowledge base by processing natural language texts. ALICE (an acronym for Automatic Learning and Inference Computerized Engine) is an attempt to model the cognitive processes that occur in humans when they learn a series of descriptive texts and reason about what they have learned. In the paper a general overview of the system is given with the descrlption of its specifics, basic methodologies, and general architecture. How parsing is performed in ALICE is illustrated by following the analysis of a sample text. I. INTRODUCTION. The capability to learn is one of the central features of intelligent behavior, and learning constitutes one of the current hot topics in artificial intelligence (Michalski, Carbonnell, and Mitchell, 1983). Much of the work on this field has dealt with induction, rule discovery, and learning by analogy or from examples, whereas much less effort has been dedicated to building systems able to learn by processing natural language texts. As Norton (1983: 308) remarked, the general agreed-upon assumption was that "such a capability is not 'learning' at all but merely (?) the conversion of knowledge from one representation to another". ACquiring new Knowledge via prose comprehension is, on the contrary, a complex activity which relies on understanding the linguistic input, storing the extracted information in memory, and integrating it with prior knowledge for effective use. As far as psychology is concerned, learning from written texts has often aroused the interest of cognitive and educational psychologists. Due to the limitations of the experimental approach which has been generally adopted, however, this topic has seldom been dealt with in its entirety. Lots of experiments have been carried on focusing on restricted arguments and specific phenomena whose explanations too often look suspiciously ad hoc. Unfortunately, those who addressed the full problem of 'meaningful verbal learning' (e.g. Ausubel, 1963) stated their theories so vaguely that it is almost impossible to express them in form of effective procedures and to implement them in computer programs. In the last few years the situation has changed and several projects (Frey, Reyle, and Rohrer, 1983; Haas and Hendrix, 1983; Nishida, Kosaka, and Doshita, 1983; Norton, 1983) are now devoted to develop computer systems which could automatically extract information from written texts. Practical applications, besides theoretical interest, motivates this kind of research. In the expert system technology, for example, the process of discovering what is known to the experts of the field in which the program must perform requires tedious and costly interactions between ~ the knowledge engineer and those experts. Automatic acquisition of knowledge by text understanding could represent a way to partially reduce the labor and fatigue involved in the transfer of …

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