Paper Info
Title
Phrase Chunking Using Entropy Guided Transformation Learning
Abstract
Entropy Guided Transformation Learning (ETL) is a new machine learning strategy that combines the advantages of decision trees (DT) and Transformation Based Learn- ing (TBL). In this work, we apply the ETL framework to four phrase chunking tasks: Por- tuguese noun phrase chunking, English base noun phrase chunking, English text chunking and Hindi text chunking. In all four tasks, ETL shows better results than Decision Trees and also than TBL with hand-crafted tem- plates. ETL provides a new training strat- egy that accelerates transformation learning. For the English text chunking task this corre- sponds to a factor of five speedup. For Por- tuguese noun phrase chunking, ETL shows the best reported results for the task. For the other three linguistic tasks, ETL shows state-of-the- art competitive results and maintains the ad- vantages of using a rule based system.
Year
Venue
Keywords
2008
ACL
machine learning,rule based system,noun phrase,transformative learning,decision tree
Field
DocType
Volume
Chunking (computing),Noun phrase,Decision tree,Rule-based system,Computer science,Phrase chunking,Speech recognition,Chunking (psychology),Natural language processing,Artificial intelligence,Transformation based learning,Speedup
Conference
P08-1
Citations 
PageRank 
References 
11
0.66
18
Authors
3
Name
Order
Citations
PageRank
Ruy Luiz Milidiú119220.18
Cícero Nogueira dos Santos277137.83
Julio C. Duarte3262.46