Text Chunking using Transformation-Based Learning
<|reference_start|>Text Chunking using Transformation-Based Learning: Eric Brill introduced transformation-based learning and showed that it can do part-of-speech tagging with fairly high accuracy. The same method can be applied at a higher level of textual interpretation for locating chunks in the tagged text, including non-recursive ``baseNP'' chunks. For this purpose, it is convenient to view chunking as a tagging problem by encoding the chunk structure in new tags attached to each word. In automatic tests using Treebank-derived data, this technique achieved recall and precision rates of roughly 92% for baseNP chunks and 88% for somewhat more complex chunks that partition the sentence. Some interesting adaptations to the transformation-based learning approach are also suggested by this application.<|reference_end|>
@article{ramshaw1995text,
    title={Text Chunking using Transformation-Based Learning},
    author={Lance A. Ramshaw (Bowdoin College) and Mitchell P. Marcus (University
  of Pennsylvania)},
    journal={ACL Third Workshop on Very Large Corpora, June 1995, pp. 82-94},
    year={1995},
    archivePrefix={arXiv},
    eprint={cmp-lg/9505040},
    primaryClass={cmp-lg cs.CL}
}