Text Chunking using Transformation-Based Learning

Lance A. Ramshaw; Mitchell P. Marcus

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arXiv · cmp-lg/9505040

Text Chunking using Transformation-Based Learning

Lance A. Ramshaw·Mitchell P. Marcus

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Abstract

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.

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Lance A. Ramshaw, Mitchell P. Marcus. 1995-05-23. Text Chunking using Transformation-Based Learning. https://arxiv.org/abs/cmp-lg/9505040

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