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We describe an approach for machine learning-based empty category detection that is based on the phrase structure analysis of Japanese. The problem is formalized as tree node classification, and we find that the path feature, the sequence of node labels from the current node to the root, is highly effective. We also find that the set of dot products between the word embeddings for a verb and those for case particles can be used as a substitution for case frames. Experiments show that the proposed method outperforms the previous state-of the art method by 68.6% to 73.2% in terms of F-measure.

Empty Category Detection using Path Features and Distributed Case Frames