Paper Info
Title
Decision Tree Learning Algorithm with structured attributes: application to verbal case frame acquisition
Abstract
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.
Year
DOI
Venue
1996
10.3115/993268.993331
international conference on computational linguistics
Keywords
Field
DocType
attribute optimally,bilingual corpus,verbal case frame acquisition,structured attribute,generalized decision tree,keen attention,decision tree learning algorithms,natural language processing research,tree generation,new dtla,conventional dtla,decision tree learning,decision tree,natural language processing,noun
Open data,Decision tree,Computer science,Noun,Tree generation,Natural language processing,Artificial intelligence,ID3 algorithm,Verb,Pattern recognition,Algorithm,Decision tree learning,Incremental decision tree
Conference
Volume
Citations 
PageRank 
C96-2
4
0.64
References 
Authors
4
1
Name
Order
Citations
PageRank
Hideki Tanaka18015.07