IMAI Mutsumi Lecturer, Environmental Information, Keio University, 環境情報学部, 専任講師 (60255601)
ISHIZAKI Shun Professor, Environmental Information, Keio University, 環境情報学部, 教授 (00245614)
|Budget Amount *help
¥2,500,000 (Direct Cost : ¥2,500,000)
Fiscal Year 1997 : ¥600,000 (Direct Cost : ¥600,000)
Fiscal Year 1996 : ¥900,000 (Direct Cost : ¥900,000)
Fiscal Year 1995 : ¥1,000,000 (Direct Cost : ¥1,000,000)
We irrvestigated the causes for difficuties adult learners face in learning a second language, with a special focus on Japanese speakers learning English as the second language. Specifically, we suspected that one of the reasons for the difficulty in learning a second language is due to the difference in lexicalzation structures between the learner's narive language and the target second language he/she is learning We tested this hypothesis empirically focusing on the two semantic domains : (1) a domain of lexicalizing motions, and (2) the domain of lexicalizing superordinate concepts with respect to individuation. Through a series of experiments, we found that, in both domains, Japanese speakers assumed novel English words as equivalents of the corresponding Japanese words, and learning of the full meanings of the target English words were hindered by this assumption.
The basic research for implementing the high quality machine translation has been conducted. A large scale association
experiment was implemented to collect gene ralized concepts, specific ones, similar ones, attribute ones, related action concepts and related situation ones from Japanese basic nouns which are extracted from elementary school text books. The concepts are organized into hierarchical structures to be an electronic concept dictionary. In general, contextual information is necessary for high quality machine translation. The dictionary will provide such crucial information from the related action concepts or the related situation concepts in the electronic concept dictionary.
As for computer simulation of human vocaburary learning mechanism, we tried to formulate it by Inductive Logic Programming with abduction (abductive ILP). Abductive ILP is different from standard ILP in a sense that it rather suggests to augment background knowledge than to infer rules to explain given examples. By using this function, the system can guess, for example, a missing grammatical category for certain words in a given sentence when it cannot be parsed correctly due to the lacking information. In our research, we developed a complete procedure to find the missing a single rule or fact if there exist. Less