Japanese Dependency Parsing using Relative Preference of Dependency
نویسندگان
چکیده
This paper presents a new statistical Japanese dependency parser which models a relative preference of dependency. Most conventional Japanese dependency parsers are based on binary classifications where all possible pairs of segments are classified into positive (dependent) or negative (non-dependent) examples. However, such methods are not suitable for dependency parsing, since the goal of this task is not to classify a pair of segments into two classes, but to select the most likely modifiee out of all candidates. The proposed method is based on this observation and models how likely a pair of segments have a dependency relation in comparison with other pairs. Experiments using the Kyoto University Corpus show that the method outperforms previous systems as well as improves the training efficiency.
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