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.
@article{arxiv.cmp-lg/9505040,
title = {Text Chunking using Transformation-Based Learning},
author = {Lance A. Ramshaw and Mitchell P. Marcus},
journal= {arXiv preprint arXiv:cmp-lg/9505040},
year = {2009}
}