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Robust Dependency Parsing of Spontaneous Japanese Spoken Language
Tomohiro OHNO Shigeki MATSUBARA Nobuo KAWAGUCHI Yasuyoshi INAGAKI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2005/03/01
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.