Semantically motivated subcategorization acquisition
Anna Korhonen · 2002
Automatic acquisition of subcategorization lexicons from textual corpora has become increasingly popular. Although this work has met with some success, resulting lexicons indicate a need for greater accuracy. One significant source of error lies in the process of hypothesis selection which is used for removing noise from automatically acquired subcategorization frames (SCFs). In this paper we describe a more accurate semantically-driven approach to hypothesis selection which can be used to improve large-scale SCF acquisition.