Extracting representative arguments from dictionaries for resolving zero pronouns
Date of Issue2005
Machine Translation Summit (10th : 2005)
School of Humanities and Social Sciences
We propose a method to alleviate the problem of referential granularity for Japanese zero pronoun resolution. We use dictionary definition sentences to extract ‘representative’ arguments of predicative definition words; e.g. ‘arrest’ is likely to take police as the subject and criminal as its object. These representative arguments are far more informative than ‘person’ that is provided by other valency dictionaries. They are auto-extracted using both Shallow parsing and Deep parsing for greater quality and quantity. Initial results are highly promising, obtaining more specific information about selectional preferences. An architecture of zero pronoun resolution using these representative arguments is described.
Machine Translation Summit X
© 2005 AAMT. This is the author created version of a work that has been peer reviewed and accepted for publication by Proceedings of Machine Translation Summit X, Asia-Pacific Association for Machine Translation. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://www.mt-archive.info/MTS-2005-Nariyama.pdf].