Association-based Natural Language Processing with Neural Networks

Association-based Natural Language Processing with Neural Networks KIMURA Kazuhiro SUZUOKA Takashi AMANO Sin-ya Information Systems Laboratory Research and Development Center TOSHIBA Corp. 1 Komukai-T6siba-ty6, Saiwai-ku, Kawasaki 210 Japan kim~isl.rdc.toshiba.co.jp Abstract This paper describes a natural language pro- cessing system reinforced by the use of associ- ation of words and concepts, implemented as a neural network. Combining an associative net- work with a conventional system contributes to semantic disambiguation in the process of interpretation. The model is employed within a kana-kanji conversion system and the advan- tages over conventional ones are shown. 1 Introduction Currently, most practical applications in nat- ural language processing (NLP) have been realized via symbolic manipulation engines, such as grammar parsers. However, the cur- rent trend (and focus of research) is shift- ing to consider aspects of semantics and dis- course as part of NLP. This can be seen in the emergence of new theories of language, such as Situation Theory [Barwise 83] and Discourse Representation Theory [Kamp 84]. While these theories provide an excellent the- oretical framework for natural language un- derstanding, the practical treatment of con- text dependency within the language can also be improved by enhancing underlying compo- nent technologies, such as knowledge based systems. In particular, alternate approaches to symbolic manipulation provided by connec- tionist models [Rumelhart 86] have emerged. Connectionist approaches enable the extrac- tion of processing knowledge from examples, instead of building knowledge bases manually. The model described here represents the unification of the connectionist approach and conventional symbolic manipulation; its most valuable feature is the use of word as- sociations using neural network technology. Word and concept associations appear to be central in human cognition [Minsky 88]. Therefore, simulating word associations con- tributes to semantic disambiguation in the computational process of interpreting sen- tences by putting a strong preference to ex- pected words(meanings). The paper describes NLP reinforced by as- sociation of concepts and words via a con- nectionist network. The model is employed within a NLP application system for kana- 224 kanji conversion x. Finally, an evaluation of the system and advantages over conventional systems are presented. 2 A brief overview of kana-kanji conversion Japanese has a several interesting feature in its variety of letters. Especially the ex- istence of several thousand of kanji (based on Chinese characters; ~, 111,..) made typing task hard before the invention of kana-kanji conversion[Amano 79] . Now it has become a standard method in inputting Japanese to computers. It is also used in word processors and is familiar to those who are not computer experts. It comes from the simpleness of op- erations. By only typing sentences by pho- netic expressions of Japanese (kan a), the kana- kanji converter automatically converts kana into meaningful expressions(kanji). The sim- plified mechanism of kana-kanji conversion can be described as two stages of processing: mor- phological analysis and homonym selection. • Morphological Analysis Kana-inputted (fragment of) sentences are morphologically analized through dic- tionary look up, both lexicons and gram- mars. There are many ambiguities in word division …

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