Extracting representative arguments from dictionaries for resolving zero pronouns

• Main Authors: Nichols, Eric, Bond, Francis, Tanaka, Takaaki, Nakaiwa, Hiromi, Nariyama, Shigeko
• Other Authors: School of Humanities and Social Sciences
• Format: Conference or Workshop Item
• Language: English
• Published: 2011
• Subjects: DRNTU::Humanities::Linguistics::Sociolinguistics::Computational linguistics; DRNTU::Humanities::Language::Japanese
• Online Access: https://hdl.handle.net/10356/79571; http://hdl.handle.net/10220/6809
• Institution: Nanyang Technological University

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.