Paper: Unsupervised Detection of Downward-Entailing Operators By Maximizing Classification Certainty

ACL ID E12-1071
Title Unsupervised Detection of Downward-Entailing Operators By Maximizing Classification Certainty
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

We propose an unsupervised, iterative method for detecting downward-entailing operators (DEOs), which are important for deducing entailment relations between sen- tences. Like the distillation algorithm of Danescu-Niculescu-Mizil et al. (2009), the initialization of our method depends on the correlation between DEOs and negative po- larity items (NPIs). However, our method trusts the initialization more and aggres- sively separates likely DEOs from spuri- ous distractors and other words, unlike dis- tillation, which we show to be equivalent to one iteration of EM prior re-estimation. Our method is also amenable to a bootstrap- ping method that co-learns DEOs and NPIs, and achieves the best results in identifying DEOs in two corpora.