Yoshio Momouchi

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We have proposed a method of machine translation, which acquires translation rules from translation examples using inductive learning, and have evaluated the method. And we have confirmed that the method requires many translation examples. To resolve this problem, we applied genetic algorithms to the method. In this paper, we describe our method with(More)
Rule-based machine translation analyzes source-language sentences using large-scale linguistic knowledge that is given by the developer beforehand. However, it is difficult to give complete linguistic knowledge to the system ex ante because natural language has various linguistic phenomena. Therefore, we worked to develop learning-based machine translation.(More)
This paper presents our research on automatic annotation of a five-billion-word corpus of Japanese blogs with information on affect and sentiment. We first perform a study in emotion blog corpora to discover that there has been no large scale emotion corpus available for the Japanese language. We choose the largest blog corpus for the language and annotate(More)
We begin this paper by putting forward the topic of human conscience as a metaphysical experience. We present our ongoing research on moral reasoning categories and make first attempts to verify their usefulness in creating an agent with a dynamic algorithm for moral reasoning. Our approach assumes creating such an agent basing on two factors, the idea of(More)
A “sentence pattern” in modern Natural Language Processing is often considered as a subsequent string of words (n-grams). However, in many branches of linguistics, like Pragmatics or Corpus Linguistics, it has been noticed that simple n-gram patterns are not sufficient to reveal the whole sophistication of grammar patterns. We present a language independent(More)
A number of machine translation systems based on the learning algorithms are presented. These methods acquire translation rules from pairs of similar sentences in a bilingual text corpora. This means that it is difficult for the systems to acquire the translation rules from sparse data. As a result, these methods require large amounts of training data in(More)
This paper presents a learning method using adjacent information as the method to extract bilingual word pairs efficiently from parallel corpora with various languages for which language resources are insufficient. In our method, information about correspondence between source language words and target language words is acquired automatically using the word(More)