Kenji Yoshimura

MWEs as non-propositional content indicators

We report that a proper employment of MWEs concerned enables us to put forth a tractable framework, which is based on a multiple nesting of semantic operations, for the processing of non-inferential, Non-propositional Contents (NPCs) of natural Japanese sentences. Our framework is characterized by its broad syntactic and semantic coverage, enabling us to deal with multiply composite modalities and their semantic/pragmatic similarity. Also, the relationship between indirect (Searle, 1975) and direct speech, and equations peculiar to modal logic and its family (Mally, 1926; Prior, 1967) are treated in the similarity paradigm.

Large Scale Collocation Data and Their Application to Japanese Word Processor Technology

Word processors or computers used in Japan employ Japanese input method through keyboard stroke combined with Kana (phonetic) character to Kanji (ideographic, Chinese) character conversion technology. The key factor of Kana-to-Kanji conversion technology is how to raise the accuracy of the conversion through the homophone processing, since we have so many homophonic Kanjis. In this paper, we report the results of our Kana-to-Kanji conversion experiments which embody the homophone processing based on large scale collocation data. It is shown that approximately 135,000 collocations yield 9.1 % raise of the conversion accuracy compared with the prototype system which has no collocation data.

Processing homonyms in the Kana-to-Kanji conversion

This paper proposes two new methods to identify the correct meaning of Japanese homonyms in text based on the noun-verb co-occurrence in a sentence which can be obtained easily from corpora. The first method uses the near co-occurrence data sets, which are constructed from the above co-occurrence relation, to select the most feasible word among homonyms in the scope of a sentence. The second uses the far co-occurrence data sets, which are constructed dynamically from the near co-occurrence data sets in the course of processing input sentences, to select the most feasible word among homonyms in the scope of a sequence of sentences. An experiment of kana-to-kanji (phonogram-to-ideograph) conversion has shown that the conversion is carried out at the accuracy rate of 79.6% per word by the first method. This accuracy rate of our method is 7.4% higher than that of the ordinary method based on the word occurrence frequency.

Development and Evaluation of a Model for Japanese Honorific Expressions Using Assembly Minutes

In this study, we describe a model of Japanese honorific expressions that is constructed automatically from assembly minutes. The Japanese language has a complicated system of honorific expressions. In assemblies in Japan, speakers usually speak using honorific expression. Hence, we create a maximum entropy model for honorific expressions from assembly minutes released by Fukuoka City, Japan. Evaluations show that our model has an accuracy of 88.2% for choosing suitable honorific expressions. In particular, it shows the best precision of 95.9% for the classification of Sonkeigo at the end of a sentence.