Exploring the Statistical Derivation of Transformational Rule Sequences for Part-of-Speech Tagging
<|reference_start|>Exploring the Statistical Derivation of Transformational Rule Sequences for Part-of-Speech Tagging: Eric 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.<|reference_end|>
@article{ramshaw1994exploring,
    title={Exploring the Statistical Derivation of Transformational Rule Sequences
  for Part-of-Speech Tagging},
    author={Lance A. Ramshaw (Univ. of Pennsylvania and Bowdoin College), and
  Mitchell P. Marcus (Univ. of Pennsylvania)},
    journal={ACL Balancing Act Workshop proceedings, July 94, pp. 86-95},
    year={1994},
    archivePrefix={arXiv},
    eprint={cmp-lg/9406011},
    primaryClass={cmp-lg cs.CL}
}