Scaling Web-based Acquisition of Entailment Relations

Scaling Web-based Acquisition of Entailment Relations Idan Szpektor⋄ idan@szpektor.net †ITC-Irst, Via Sommarive, 18 (Povo) - 38050 Trento, Italy ‡DIT - University of Trento, Via Sommarive, 14 (Povo) - 38050 Trento, Italy ◦Department of Computer Science, Bar Ilan University - Ramat Gan 52900, Israel ⋄Department of Computer Science, Tel Aviv University - Tel Aviv 69978, Israel Hristo Tanev† tanev@itc.it Ido Dagan◦ dagan@cs.biu.ac.il Bonaventura Coppola†‡ coppolab@itc.it Abstract Paraphrase recognition is a critical step for nat- ural language interpretation. Accordingly, many NLP applications would benefit from high coverage knowledge bases of paraphrases. However, the scal- ability of state-of-the-art paraphrase acquisition ap- proaches is still limited. We present a fully unsuper- vised learning algorithm for Web-based extraction of entailment relations, an extended model of para- phrases. We focus on increased scalability and gen- erality with respect to prior work, eventually aiming at a full scale knowledge base. Our current imple- mentation of the algorithm takes as its input a verb lexicon and for each verb searches the Web for re- lated syntactic entailment templates. Experiments show promising results with respect to the ultimate goal, achieving much better scalability than prior Web-based methods. 1 Introduction Modeling semantic variability in language has drawn a lot of attention in recent years. Many ap- plications like QA, IR, IE and Machine Translation (Moldovan and Rus, 2001; Hermjakob et al., 2003; Jacquemin, 1999) have to recognize that the same meaning can be expressed in the text in a huge vari- ety of surface forms. Substantial research has been dedicated to acquiring paraphrase patterns, which represent various forms in which a certain meaning can be expressed. Following (Dagan and Glickman, 2004) we ob- serve that a somewhat more general notion needed for applications is that of entailment relations (e.g. (Moldovan and Rus, 2001)). These are directional relations between two expressions, where the mean- ing of one can be entailed from the meaning of the other. For example “X acquired Y” entails “X owns Y”. These relations provide a broad framework for representing and recognizing semantic variability, as proposed in (Dagan and Glickman, 2004). For example, if a QA system has to answer the question “Who owns Overture?” and the corpus includes the phrase “Yahoo acquired Overture”, the system can use the known entailment relation to conclude that this phrase really indicates the desired answer. More examples of entailment relations, acquired by our method, can be found in Table 1 (section 4). To perform such inferences at a broad scale, ap- plications need to possess a large knowledge base (KB) of entailment patterns. We estimate such a KB should contain from between a handful to a few dozens of relations per meaning, which may sum to a few hundred thousands of relations for a broad domain, given that a typical lexicon includes tens of thousands of words. Our research goal is to approach unsupervised ac- quisition of such a full scale KB. We focus on de- veloping methods that acquire entailment relations from the Web, the largest available resource. To this …

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