Satoshi Nambu

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2018

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Predicting Japanese Word Order in Double Object Constructions
Masayuki Asahara | Satoshi Nambu | Shin-Ichiro Sano
Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing

This paper presents a statistical model to predict Japanese word order in the double object constructions. We employed a Bayesian linear mixed model with manually annotated predicate-argument structure data. The findings from the refined corpus analysis confirmed the effects of information status of an NP as ‘givennew ordering’ in addition to the effects of ‘long-before-short’ as a tendency of the general Japanese word order.
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