In this paper we describe a method of acquiring word order fl'om corpora. Word order is defined as the order of modifiers, or the order of phrasal milts called 'bunsetsu' which depend on the stone modifiee. The method uses a model which automatically discovers what the tendency of the word order in Japanese is by using various kinds of information in and around the target bunsetsus. This model shows us to what extent each piece of information contributes to deciding the word order mid which word order tends to be selected when several kinds of information conflict. The contribution rate of each piece of information in deciding word order is eiIiciently learned by a model within a maximum entropy framework. The performance of this traiimd model can be ewfluated by checking how many instances of word order stletted by the model agree with those in the original text. In this paper, we show t, hat even a raw corpits that has not been tagged can be used to train the model, if it is first analyzed by a parser. This is possible because the word order of the text in the corpus is correct.
Uchimoto et al. performed a study of sentence generation to estimate the order of words on the basis of the phrase dependency information using the maximum entropy method REF .
Word Order Acquisition From Corpora
International Conference On Computational Linguistics
Computer Science