Hideki Ogura


A Japanese Word Dependency Corpus
Shinsuke Mori | Hideki Ogura | Tetsuro Sasada
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper, we present a corpus annotated with dependency relationships in Japanese. It contains about 30 thousand sentences in various domains. Six domains in Balanced Corpus of Contemporary Written Japanese have part-of-speech and pronunciation annotation as well. Dictionary example sentences have pronunciation annotation and cover basic vocabulary in Japanese with English sentence equivalent. Economic newspaper articles also have pronunciation annotation and the topics are similar to those of Penn Treebank. Invention disclosures do not have other annotation, but it has a clear application, machine translation. The unit of our corpus is word like other languages contrary to existing Japanese corpora whose unit is phrase called bunsetsu. Each sentence is manually segmented into words. We first present the specification of our corpus. Then we give a detailed explanation about our standard of word dependency. We also report some preliminary results of an MST-based dependency parser on our corpus.


Design, Compilation, and Preliminary Analyses of Balanced Corpus of Contemporary Written Japanese
Kikuo Maekawa | Makoto Yamazaki | Takehiko Maruyama | Masaya Yamaguchi | Hideki Ogura | Wakako Kashino | Toshinobu Ogiso | Hanae Koiso | Yasuharu Den
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Compilation of a 100 million words balanced corpus called the Balanced Corpus of Contemporary Written Japanese (or BCCWJ) is underway at the National Institute for Japanese Language and Linguistics. The corpus covers a wide range of text genres including books, magazines, newspapers, governmental white papers, textbooks, minutes of the National Diet, internet text (bulletin board and blogs) and so forth, and when possible, samples are drawn from the rigidly defined statistical populations by means of random sampling. All texts are dually POS-analyzed based upon two different, but mutually related, definitions of ‘word.’ Currently, more than 90 million words have been sampled and XML annotated with respect to text-structure and lexical and character information. A preliminary linear discriminant analysis of text genres using the data of POS frequencies and sentence length revealed it was possible to classify the text genres with a correct identification rate of 88% as far as the samples of books, newspapers, whitepapers, and internet bulletin boards are concerned. When the samples of blogs were included in this data set, however, the identification rate went down to 68%, suggesting the considerable variance of the blog texts in terms of the textual register and style.


A Proper Approach to Japanese Morphological Analysis: Dictionary, Model, and Evaluation
Yasuharu Den | Junpei Nakamura | Toshinobu Ogiso | Hideki Ogura
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In this paper, we discuss lemma identification in Japanese morphological analysis, which is crucial for a proper formulation of morphological analysis that benefits not only NLP researchers but also corpus linguists. Since Japanese words often have variation in orthography and the vocabulary of Japanese consists of words of several different origins, it sometimes happens that more than one writing form corresponds to the same lemma and that a single writing form corresponds to two or more lemmas with different readings and/or meanings. The mapping from a writing form onto a lemma is important in linguistic analysis of corpora. The current study focuses on disambiguation of heteronyms, words with the same writing form but with different word forms. To resolve heteronym ambiguity, we make use of goshu information, the classification of words based on their origin. Founded on the fact that words of some goshu classes are more likely to combine into compound words than words of other classes, we employ a statistical model based on CRFs using goshu information. Experimental results show that the use of goshu information considerably improves the performance of heteronym disambiguation and lemma identification, suggesting that goshu information solves the lemma identification task very effectively.