Paper: Decision Tree Learning Algorithm With Structured Attributes: Application To Verbal Case Frame Acquisition

ACL ID C96-2159
Title Decision Tree Learning Algorithm With Structured Attributes: Application To Verbal Case Frame Acquisition
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1996
Authors

The Decision Tree Learning Algorithms (DTLAs) are getting keen attention from the natural language processing re- search comlnunity, and there have been a series of attempts to apply them to verbal case frame acquisition. However, a DTLA cannot handle structured at- tributes like nouns, which are classified under a thesaurus. In this paper, we present a new DTLA that can ratio- nally handle the structured attributes. In the process of tree generation, the algorithm generalizes each attribute op- timally using a given thesaurus. We ap- ply this algorithm to a bilingual corpus and show that it successfiflly learned a generalized decision tree for classifying the verb "take" and that the tree was smaller with more prediction power on the open data than the tree learned by the conventional DTL...