PP-attachment disambiguation boosted by a gigantic volume of unambiguous examples

Abstract

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. © Springer-Verlag Berlin Heidelberg 2005.