Interpreting anaphoric shell nouns using antecedents of cataphoric shell nouns as training data
Published in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 2013
Recommended citation: Varada Kolhatkar, Heike Zinsmeister, and Graeme Hirst. 2013. Interpreting anaphoric shell nouns using antecedents of cataphoric shell nouns as training data. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 300–310, Seattle, Washington, USA, October. Association for Computational Linguistics. http://www.anthology.aclweb.org/D/D13/D13-1030.pdf
Interpreting anaphoric shell nouns (ASNs) such as this issue and this fact is essential to understanding virtually any substantial natural language text. One obstacle in developing methods for automatically interpreting ASNs is the lack of annotated data. We tackle this challenge by exploiting cataphoric shell nouns (CSNs) whose construction makes them particularly easy to interpret (e.g., the fact that X). We propose an approach that uses automatically extracted antecedents of CSNs as training data to interpret ASNs. We achieve precisions in the range of 0.35 (baseline = 0.21) to 0.72 (baseline = 0.44), depending upon the shell noun.