Paper: Learning In Natural Language: Theory And Algorithmic Approaches

ACL ID W00-0701
Title Learning In Natural Language: Theory And Algorithmic Approaches
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2000
Authors
  • Dan Roth (University of Illinois at Urbana-Champaign, Urbana IL)

This article summarizes work on developing a learning theory account for the major learning and statistics based approaches used in natural language processing. It shows that these ap- proaches can all be explained using a single dis- tribution free inductive principle related to the pac model of learning. Furthermore, they all make predictions using the same simple knowl- edge representation - a linear representation over a common feature space. This is signifi- cant both to explaining the generalization and robustness properties of these methods and to understanding how these methods might be ex- tended to learn from more structured, knowl- edge intensive examples, as part of a learning centered approach to higher level natural lan- guage inferences.