@article{oai:nagoya.repo.nii.ac.jp:00006184,
 author = {Ohno, Tomohiro and Matsubara, Shigeki and Kawaguchi, Nobuo and Inagaki, Yasuyoshi},
 issue = {3},
 journal = {IEICE Transactions on Information and Systems},
 month = {Mar},
 note = {Spontaneously spoken Japanese includes a lot of grammatically ill-formed linguistic phenomena such as fillers, hesitations, inversions, and so on, which do not appear in written language. This paper proposes a novel method of robust dependency parsing using a large-scale spoken language corpus, and evaluates the availability and robustness of the method using spontaneously spoken dialogue sentences. By utilizing stochastic information about the appearance of ill-formed phenomena, the method can robustly parse spoken Japanese including fillers, inversions, or dependencies over utterance units. Experimental results reveal that the parsing accuracy reached 87.0 ％, and we confirmed that it is effective to utilize the location information of a bunsetsu, and the distance information between bunsetsus as stochastic information.},
 pages = {545--552},
 title = {Robust Dependency Parsing of Spontaneous Japanese Spoken Language},
 volume = {E88-D},
 year = {2005}
}