Unsupervised Detection of Downward-Entailing Operators By Maximizing Classification Certainty

Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 696–705, Avignon, France, April 23 - 27 2012. c⃝2012 Association for Computational Linguistics Unsupervised Detection of Downward-Entailing Operators By Maximizing Classification Certainty Jackie CK Cheung and Gerald Penn Department of Computer Science University of Toronto Toronto, ON, M5S 3G4, Canada {jcheung,gpenn}@cs.toronto.edu Abstract 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. 1 Introduction Reasoning about text has been a long-standing challenge in NLP, and there has been consider- able debate both on what constitutes inference and what techniques should be used to support infer- ence. One task involving inference that has re- cently received much attention is that of recog- nizing textual entailment (RTE), in which the goal is to determine whether a hypothesis sentence can be entailed from a piece of source text (Bentivogli et al., 2010, for example). An important consideration in RTE is whether a sentence or context produces an entailment re- lation for events that are a superset or subset of the original sentence (MacCartney and Manning, 2008). By default, contexts are upward-entailing, allowing reasoning from a set of events to a su- perset of events as seen in (1). In the scope of a downward-entailing operator (DEO), however, this entailment relation is reversed, such as in the scope of the classical DEO not (2). There are also operators which are neither upward- nor downward entailing, such as the expression ex- actly three (3). (1) She sang in French. ⇒ She sang. (upward-entailing) (2) She did not sing in French. ⇐ She did not sing. (downward-entailing) (3) Exactly three students sang. ̸⇔ Exactly three students sang in French. (neither upward- nor downward-entailing) Danescu-Niculescu-Mizil et al. (2009) (hence- forth DLD09) proposed the first computational methods for detecting DEOs from a corpus. They proposed two unsupervised algorithms which rely on the correlation between DEOs and negative polarity items (NPIs), which by the definition of Ladusaw (1980) must appear in the context of DEOs. An example of an NPI is yet, as in the sentence This project is not complete yet. The first baseline method proposed by DLD09 sim- ply calculates a ratio of the relative frequencies of a word in NPI contexts versus in a general corpus, and the second is a distillation method which appears to refine the baseline ratios using a task-specific heuristic. Danescu-Niculescu-Mizil and Lee (2010) (henceforth DL10) extend …

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