Marus Uneson
Centre for Languages and Literature, Lund University, Sweden
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Published in: Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013); May 22-24; 2013; Oslo University; Norway. NEALT Proceedings Series 16
Linköping Electronic Conference Proceedings 85:36, p. 399-409
NEALT Proceedings Series 16:36, p. 399-409
Published: 2013-05-17
ISBN: 978-91-7519-589-6
ISSN: 1650-3686 (print), 1650-3740 (online)
The RWAAI (Repository and Workspace for Austroasiatic Intangible heritage) project aims at building a digital archive out of existing legacy data from the Austroasiatic language family. One aspect of the project is the preservation of analogue legacy data. In this context; we have at our hands a large number of mostly-phonemic transcriptions of narrative monologues; often with accompanying sound recordings; in the unwritten Kammu language of northern Laos. Some of the transcriptions; however; lack tone marks; which for a tonal language such as Kammu makes them substantially less useful. The problem of restoring tones can be recast as one of word sense disambiguation; or; more generally; lexical ambiguity resolution. We attack it by decision lists; along the lines of Yarowsky (1994); using the tone-marked part of the corpus (120kW) as training data. The performance ceiling of this corpus is uncertain: the stories were all annotated; primarily for human rather than machine consumption; by a single person during almost 40 years; with slowly emerging idiosyncratic conventions. Thus; both inter-annotator and intra-annotator agreement figures are unknown. Nevertheless; with the data from this one annotator as a gold standard; we improve from an already-high baseline accuracy of 95.7% to 97.2% (by 10-fold cross-validation).
Word sense disambiguation; Kammu; decision lists; lexical ambiguity resolution; tone restoration; legacy data
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