Robust Dependency Parsing of Spontaneous Japanese Spoken Language

Tomohiro OHNO  Shigeki MATSUBARA  Nobuo KAWAGUCHI  Yasuyoshi INAGAKI  

IEICE TRANSACTIONS on Information and Systems   Vol.E88-D    No.3    pp.545-552
Publication Date: 2005/03/01
Online ISSN: 
DOI: 10.1093/ietisy/e88-d.3.545
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Corpus-Based Speech Technologies)
Category: Speech Corpora and Related Topics
dependency parsing,  stochastic parsing,  Japanese speech,  linguistic phenomena,  syntactically annotated corpus,  

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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.

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