Exploring the Statistical Derivation of Transformational Rule Sequences for Part-of-Speech Tagging

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dc.creatorRamshaw, Lance A.
dc.creatorMarcus, Mitchell P.
dc.date1994-06-03
dc.date.accessioned2026-07-07T09:09:19Z
dc.date.available2026-07-07T09:09:19Z
dc.descriptionEric Brill has recently proposed a simple and powerful corpus-based language modeling approach that can be applied to various tasks including part-of-speech tagging and building phrase structure trees. The method learns a series of symbolic transformational rules, which can then be applied in sequence to a test corpus to produce predictions. The learning process only requires counting matches for a given set of rule templates, allowing the method to survey a very large space of possible contextual factors. This paper analyses Brill's approach as an interesting variation on existing decision tree methods, based on experiments involving part-of-speech tagging for both English and ancient Greek corpora. In particular, the analysis throws light on why the new mechanism seems surprisingly resistant to overtraining. A fast, incremental implementation and a mechanism for recording the dependencies that underlie the resulting rule sequence are also described.
dc.description10 pages, in proceedings of the ACL Balancing Act workshop
dc.identifierhttps://arxiv.org/abs/cmp-lg/9406011
dc.identifierhttp://arxiv.org/abs/cmp-lg/9406011
dc.identifierACL Balancing Act Workshop proceedings, July 94, pp. 86-95
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/150995
dc.subjectComputation and Language
dc.titleExploring the Statistical Derivation of Transformational Rule Sequences for Part-of-Speech Tagging
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