抄録
Words are communication media to share a concept in a community (e.g. an industrial company and research group). A word involving ambiguity represents multiple concepts (or meanings) depending on a context. Such ambiguity causes misunderstanding between people having different contexts. On the other hands, a community uses words to obtain responses and/or evaluations from target population, such as customers and participants. The word ambiguity causes misunderstanding between a community and a target population due to different contexts. A community dealing with multiple languages (e.g. multinationals) has a difficulty in translation if there are no words in a second language, all meanings of which do not correspond to all meanings of a word one wishes to translate. To deal with above issues caused by word ambiguity, we propose a multilingual semantic networks(MLSN) framework in this paper. The MLSN is a graph where multiple languages words, as nodes, are semantically linked through concepts, as another type nodes. We implemented MLSN in a graph database with datasets of WordNet in three languages: English, Japanese, and French. With MLSN, we conducted two analysis. In the first analysis, we investigate the meanings of ambiguous words such as “design” and Japanese word “Kansei”, and their semantic relations with relevant words in other languages. We found that there are no words corresponding to all meanings of those words in second languages. For the word “Kansei”, we illustrate semantic relations with words such as “emotion”, “affect”, “feeling”, “impression”, and “intuition” which are often used to define “Kansei”. In the second analysis, we discuss how MLSN supports to select and translate a set of words used as evaluation descriptors. We analyze 10 positive emotion words from well-established Geneva Emotion Wheel and their translation in French and Japanese. We demonstrate how MLSN automatically find translation mismatches and semantic independence between emotion descriptors. Based on discussion through abovementioned analysis, we propose a multilingual word suggestion system that can be used for both disambiguation and translation support.
