Text Chunking using Transformation-Based Learning
Computing Research Repository(1999)
摘要
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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