Improving the accuracy of subcategorizations acquired from corpora
Naoki Yoshinaga · 2004
This paper presents a method of improving the accuracy of subcategorization frames (SCFs) acquired from corpora to augment existing lexicon resources. I estimate a confidence value of each SCF using corpus-based statistics, and then perform clustering of SCF confidence-value vectors for words to capture cooccurrence tendency among SCFs in the lexicon. I apply my method to SCFs acquired from corpora using lexicons of two large-scale lexicalized grammars. The resulting SCFs achieve higher precision and recall compared to SCFs obtained by naive frequency cut-off.