Automatic Learning of Textual Entailments with Cross-Pair Similarities

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 401–408, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Automatic learning of textual entailments with cross-pair similarities Fabio Massimo Zanzotto DISCo University of Milano-Bicocca Milan, Italy zanzotto@disco.unimib.it Alessandro Moschitti Department of Computer Science University of Rome “Tor Vergata” Rome, Italy moschitti@info.uniroma2.it Abstract In this paper we define a novel similarity measure between examples of textual en- tailments and we use it as a kernel func- tion in Support Vector Machines (SVMs). This allows us to automatically learn the rewrite rules that describe a non trivial set of entailment cases. The experiments with the data sets of the RTE 2005 challenge show an improvement of 4.4% over the state-of-the-art methods. 1 Introduction Recently, textual entailment recognition has been receiving a lot of attention. The main reason is that the understanding of the basic entailment pro- cesses will allow us to model more accurate se- mantic theories of natural languages (Chierchia and McConnell-Ginet, 2001) and design important applications (Dagan and Glickman, 2004), e.g., Question Answering and Information Extraction. However, previous work (e.g., (Zaenen et al., 2005)) suggests that determining whether or not a text T entails a hypothesis H is quite complex even when all the needed information is explic- itly asserted. For example, the sentence T1: “At the end of the year, all solid companies pay divi- dends.” entails the hypothesis H1: “At the end of the year, all solid insurance companies pay divi- dends.” but it does not entail the hypothesis H2: “At the end of the year, all solid companies pay cash dividends.” Although these implications are uncontrover- sial, their automatic recognition is complex if we rely on models based on lexical distance (or sim- ilarity) between hypothesis and text, e.g., (Corley and Mihalcea, 2005). Indeed, according to such approaches, the hypotheses H1 and H2 are very similar and seem to be similarly related to T1. This suggests that we should study the properties and differences of such two examples (negative and positive) to derive more accurate entailment mod- els. For example, if we consider the following en- tailment: T3 ⇒ H3? T3 “All wild animals eat plants that have scientifically proven medicinal proper- ties.” H3 “All wild mountain animals eat plants that have scientifically proven medici- nal properties.” we note that T3 is structurally (and somehow lex- ically similar) to T1 and H3 is more similar to H1 than to H2. Thus, from T1 ⇒ H1 we may extract rules to derive that T3 ⇒ H3. The above example suggests that we should rely not only on a intra-pair similarity between T and H but also on a cross-pair similarity between two pairs (T ′, H′) and (T ′′, H′′). The latter similarity measure along with a set of annotated examples al- lows a learning algorithm to automatically derive syntactic and lexical rules that can solve complex entailment cases. In this paper, we define a new cross-pair similar- ity measure based …

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