Decision Tree Learning Algorithm with structured attributes
Hideki Tanaka · 1996
The Decision Tree Learning Algorithms (DTLAs) are getting keen attention from the natural language processing research community, 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 successfully 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.