Abstract
We propose a machine learning-based method for analyzing coordinate structure in Japanese sentences. Effective methods for disambiguating coordination scopes already exist for English, but these methods assume input sentences always contain coordinations. Since detecting coordinations is non-trivial in Japanese, this assumption is often violated. The proposed method mitigates this problem by detecting the presence of coordinations and disambiguating their scopes simultaneously. It is built upon the previous work on English coordination that uses alignment graphs to evaluate the similarity of conjuncts. A ``bypass'' is introduced in these graphs to explicitly represent the non-existence of coordinations in a sentence, so that the feature weights for coordinations are learned separately from the weights for sentences not containing coordinations. We also propose to make all features dependent on the distance between conjuncts. In an experiment with the EDR corpus, the proposed method outperforms existing methods.
