Context Analysis System for Japanese Text

A natural language understanding system is described which extracts contextual information from Japanese texts. It integrates syntactic, semantic and contextual processing serially. The syntactic analyzer obtains rough syntactic structures from the text. The semantic analyzer treats modifying relations inside noun phrases and case relations among verbs and noun phrases. Then, the contextual analyzer obtains contextual information from the semantic structure extracted by the semantic analyzer. Our system understands the context using preceded contextual knowledge on terrorism and plugs the event information in input sentences into the contextual structure. i: Introduction Despite the advanced state of syntactic analysis research for natural language processing and the many useful results it has produced, there have been few studies involving contextual information, and many problems remain unsolved. The natural language understanding system described here employs a syntactic analyzer, a semantic analyzer treating modifying relations inside noun phrases and the relations among verbs and phrases, that is, word-level semantics, and a contextual analyzer (Fig. i). These analyzers operate in a serially integrated fashion. Though humans seem to understand natural language texts using these three analyzers simultaneously, we have made their methodology essentially different from their human counterparts for more efficient computing. Our system uses a context-free grammar parser named Extended-Lingol as a syntactic analyzer to analyze the Japanese sentences and produce parsing trees. From an analysis of these, in turn, it obtains word-level semantic structures expressed in frame-like representations. Finally, it extracts contextual information, using our representation from the semantic structures. We remain far from certain at this stage whether this system represents the best realization of an engineering-based natural language understanding system. Future plans include combining these three processes into one process and bringing the system closer to the human process. Because our system uses bottom-up analysis first (including syntactic analysis and word-level semantic analysis), it can obtain not only the outline of the input sentences but also their details, as necessary. This method is the best one in situations where the detailed information of texts are quite important, such as Machine-Translation systems and precise question-answering systems. Of course, in this way, we must build up a sizable dictionary of precise word definitions. In our system, predictive-style processing is not used in syntactic analysis and word-level semantic analysis. But, in the contextual analysis part, predictions from the tree structure of the contextual information are used for instantiation of the contextual structure. We are now developing a system which can understand newspaper articles through contextual structure (see Fig. 2a). After applying the procedures outlined above, the system obtains I Input sost0nces ] (Nouspa2er ar t ~cles| in Japanese)