PP-Attachment Disambiguation Boosted by a Gigantic Volume of Unambiguous Examples

We present a PP-attachment disambiguation method based on a gigantic volume of unambiguous examples extracted from raw corpus. The unambiguous examples are utilized to acquire precise lexical preferences for PP-attachment disambiguation. Attachment decisions are made by a machine learning method that optimizes the use of the lexical preferences. Our experiments indicate that the precise lexical preferences work effectively.

• Type: Conference
• Year: 2005
• Where: IJCNLP
• Authors: Daisuke Kawahara, Sadao Kurohashi
• Keywords: IJCNLP 2005, Natural Language Processing, PP-attachment Disambiguation, Precise Lexical Preferences, Unambiguous Examples