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Text Chunking Using Transformation-Based Learning

  • L. A. Ramshaw
  • M. P. Marcus
Part of the Text, Speech and Language Technology book series (TLTB, volume 11)

Abstract

Transformation-based learning, a technique introduced by Eric Brill (1993b), has been shown to do part-of-speech tagging with fairly high accuracy. This 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 93% for baseNP chunks (trained on 950K words) and 88% for somewhat more complex chunks that partition the sentence (trained on 200K words). Working in this new application and with larger template and training sets has also required some interesting adaptations to the transformation-based learning approach.

Keywords

Noun Phrase Prepositional Phrase Lexical Rule Apply Natural Language Processing Rule Template 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1999

Authors and Affiliations

  • L. A. Ramshaw
  • M. P. Marcus

There are no affiliations available

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