Building Japanese-English Dictionary based on Ontology for Machine Translation

Building Japanese-English Dictionary based on Ontology for Machine Translation Akitoshi Okumura, Eduard Hovy USC/Information Sciences Institute 4676 Admiralty Way Marina del Rey, CA 90292 ABSTRACT This paper describes a semi-automatic method for associating a Japanese lexicon with a semantic concept taxonomy called an ontology, using a Japanese-English bilingual dictionary as a "bridge". The ontology supports semantic processing in a knowledge-based machine translation system by providing a set of language-neutral symbols and semantic information. To put the ontology to practical use, lexical items of each language of interest must be linked to appropriate ontology items. The association of ontology items with lexical items of various languages is a process fraught with difficulty: since much of this work depends on the subjective decisions of hu- man workers, large MT dictionaries tend to be subject to some dispersion and inconsistency. The problem we focus on here is how to associate concepts in the ontology with Japanese lexical entities by automatic methods, since it is too difficult to define adequately many concepts manually. We have designed three algorithms to associate a Japanese lexicon with the concepts of the ontology automatically: the equivalent-word match, the argument match, and the exam- ple match. We simulated these algorithms for 980 nouns, 860 verbs and 520 adjectives as preliminary experiments. The al- gorithms axe found to be effective for more than 80% of the words. 1. Introduction This paper describes a semi-automatic method for asso- ciating a Japanese lexicon with a semantic concept tax- onomy using a Japanese-English bilingual dictionary as a "bridge", in order to support semantic processing in a knowledge-based machine translation (MT) system. To enhance the semantic processing in MT systems, many system include conceptual networks called ontolo- gies or semantic taxonomies [Bateman, 1990; Carlson and Nirenburg, 1990; Hovy and Knight, 1993; Klavans et al., 1990; Klavans et al., 1991; Knight, 1993]. These ontologies house the representation symbols used by the analyzer and generator. To put the ontologies to practi- cal use, lexical items of each language of interest should be linked to appropriate ontology items. To support ex- tensibility to new languages, the MT ontology should be language-neutral, if not language-independent[Hovy and Nirenburg, 1992]. However, the construction of language-neutral ontologies, and the association of on- tology items with lexical items of various languages, are processes fraught with difficulty. Much of this work de- pends on the subjective decisions of more than one hu- man workers. Therefore, large MT dictionaries tend to be subject to some dispersion and inconsistency. Many translation errors are due to these dictionary problems, because the quality of the MT dictionaries are essential for the translation process. If possible, the dictionary quality should be controlled by automatic algorithms during the process of development to suppress disper- sions and inconsistencies, even if the final check should be entrusted to the human workers. Another motivation for the development of automated dictionary/ontology alignment algorithms is the in- creased availability of online lexical and semantic re- sources, such as lexicons, taxonomies, dictionaries and thesaiuri[Matsumoto et …

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