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.
CITATION STYLE
Ramshaw, L. A., & Marcus, M. P. (1999). Text Chunking Using Transformation-Based Learning (pp. 157–176). https://doi.org/10.1007/978-94-017-2390-9_10
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