Paper: Semi-supervised CCG Lexicon Extension

ACL ID D11-1115
Title Semi-supervised CCG Lexicon Extension
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

This paper introduces Chart Inference (CI), an algorithm for deriving a CCG category for an unknown word from a partial parse chart. It is shown to be faster and more pre- cise than a baseline brute-force method, and to achieve wider coverage than a rule-based system. In addition, we show the application of CI to a domain adaptation task for ques- tion words, which are largely missing in the Penn Treebank. When used in combination with self-training, CI increases the precision of the baseline StatCCG parser over subject- extraction questions by 50%. An error analy- sis shows that CI contributes to the increase by expanding the number of category types avail- able to the parser, while self-training adjusts the counts.