Phrase Chunking Using Entropy Guided Transformation Learning

Entropy Guided Transformation Learning (ETL) is a new machine learning strategy that combines the advantages of decision trees (DT) and Transformation Based Learning (TBL). In this work, we apply the ETL framework to four phrase chunking tasks: Portuguese 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 templates. ETL provides a new training strategy that accelerates transformation learning. For the English text chunking task this corresponds to a factor of five speedup. For Portuguese noun phrase chunking, ETL shows the best reported results for the task. For the other three linguistic tasks, ETL shows state-of-theart competitive results and maintains the advantages of using a rule based system.

• Type: Conference
• Year: 2008
• Where: ACL
• Authors: Ruy Luiz Milidiú, Cícero Nogueira dos Santos, Julio C. Duarte
• Keywords: ACL 2008, Computational Linguistics, English Text Chunking, Phrase Chunking, Text Chunking