Decision Tree Learning Algorithm with Structured Attributes: Application to Verbal Case Frame Acquisition

The Decision Tree Learning Algorithms (DTLAs) are getting keen attention from the natural language processing research comlnunity, and there have been a series of attempts to apply them to verbal case frame acquisition. However, a DTLA cannot handle structured attributes like nouns, which are classified under a thesaurus. In this paper, we present a new DTLA that can rationally handle the structured attributes. In the process of tree generation, the algorithm generalizes each attribute optimally using a given thesaurus. We apply 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 DTLA.

• Type: Conference
• Year: 1996
• Where: COLING
• Authors: Hideki Tanaka
• Keywords: COLING 1996, COLING 2008, Decision Tree, Decision Tree Learning, Structured Attributes