Analysis of Scene Identification Ability of Associative Memory with Pictorial Dictionary

Analysis of Scene Identification Ability of Associative Memory with Pictorial Dictionary Tatsuhiko TSUNODA *, I-Iidehiko TANAKA Tanaka Hidehiko Laboratory, Departlnent of Electrical Engineering Faculty of Engineering, University of Tokyo, 7-3-1 Hongo, BunkyoqCu, Tokyo 113, Japan {tsunoda,t an aka} Qmtl. t. u-tokyo, ac.j I) Abstract Semantic disambiguation depends on a process of defining the appropriate knowledge context. Recent research directions suggest a eonnectionist approach which use dictionaries, but there remain problems of scale, analysis, and interpretation. IIere we focus on word disambiguation as scene selection, based on the Oxford Pictorial English Dictionary. We present a re- sults of a spatial-scene identification ability using our original associative mcmor~j, We show both theoretical and experimental analysis, based on a several different measures including information entropy. 1 Introduction 'the difficulty of semantic disambiguation in natural language processing originates with the complexity of defining disambiguating knowledge contexts (Barwise J. and Perry J., 1983). These knowledge contexts must provide unique interpretations for co-dependent words, and help resolve "semantic garden path" se- quences. For example, in "John shot some bucks,"a unique reading requires semantic agreement on "shot" and "bucks," suggesting either a hunting or gambling context. The semantic garden path can be illustrated by prefixing the above sentence with "John travelled to the woods," which might suggest the hunting context, but then appending "The illegal csmino was hidden far from town," to dramatically change the interpretation suggested by the first two sentences. The core of the problem is the disciplined and dy- namic construction of a disambiguating kvowledge context. While it might be possible to write static rules which provide disambiguating information in the context of complete knowledge, such rulmhased mod- els are both time and space inefficient. Recognizing these problems, Waltz D.L. and Pollack J.B.(1985) and Cottrell G.W.(1989) proposed a f,~sci- hating connectionist approach, which uses early ideas from semantic networks to resolve semantic ambiguity *Supported by the Fellowships of the Japan Society for the Promotion of Science for Japanese Junior Scientists by dynamic spreading activation. This spreading acti- wttion construction of disambiguating context is based oil a high density associative cognitive model, but still has problems: (1) no automated learning method to adaptively construct the model, (2) non-scalable, and (3) no method of confirming hypothesized dis- ambiguation. Shastri L.(1988) proposes a similar structure, which uses st statistical semantic network. Sharkey N.E. (1989) has proposed a system for process- ing script-based narratives based on combining local representation and relaxation techniques with ImrMlel distributed learning and mapping mechanisms. Mi- ikkulainen's system DISCERN(Miikkulainen R., 1993) is also suggestive of adaptive processing, and uses self- organizing representation of words and memory de- pending on semantics. However, all of these models share the problems enumerated above. Research directions for improvements suggest the use of existing collections of machine-readM~le dictio- naries. Ilecently, Nishlklmi M. et al. (1992) has pro- posed a new relationship between language acquistion and learning based on scene analaysis. Furthermore, Bookman L.A.(1993) has proposed a scalable architec- ture for integrating ~tqsociative and semantic memory using a thesaurus. Based …

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