Paper: A Report Of Recent Progress In Transformation-Based Error-Driven Learning

ACL ID H94-1049
Title A Report Of Recent Progress In Transformation-Based Error-Driven Learning
Venue Human Language Technologies
Session Main Conference
Year 1994
Authors
  • Eric Brill (Massachusetts Institute of Technology, Cambridge MA)

Most recent research in trainable part of speech taggers has explored stochastic tagging. While these taggers obtain high accuracy, linguistic information is captured indirectly, typi- cally in tens of thousands of lexical and contextual probabili- ties. In [Brill 92], a trainable rule-based tagger was described that obtained performance comparable to that of stochas- tic taggers, but captured relevant linguistic information in a sma]_l number of simple non-stochastic rules. In this pa- per, we describe a number of extensions to this rule-based tagger. First, we describe a method for expressing lexical re- lations in tagging that stochastic taggers are currently unable to express. Next, we show a rule-based approach to tagging unknown words. Finally, we show how the tagger can be extended ...