2011-acl-world-knowledge-for-rte - Types of Common-Sense...

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Types of Common-Sense Knowledge Needed for Recognizing Textual Entailment Peter LoBue and Alexander Yates Temple University Broad St. and Montgomery Ave. Philadelphia, PA 19130 { peter.lobue,yates } @temple.edu Abstract Understanding language requires both linguis- tic knowledge and knowledge about how the world works, also known as common-sense knowledge. We attempt to characterize the kinds of common-sense knowledge most often involved in recognizing textual entailments. We identify 20 categories of common-sense knowledge that are prevalent in textual entail- ment, many of which have received scarce at- tention from researchers building collections of knowledge. 1 Introduction It is generally accepted that knowledge about how the world works, or common-sense knowledge, is vital for natural language understanding. There is, however, much less agreement or understanding about how to define common-sense knowledge, and what its components are (Feldman, 2002). Existing large-scale knowledge repositories, like Cyc (Guha and Lenat, 1990), OpenMind (Stork, 1999), and Freebase 1 , have steadily gathered together impres- sive collections of common-sense knowledge, but no one yet believes that this job is done. Other da- tabases focus on exhaustively cataloging a specific kind of knowledge — e.g. , synonymy and hyper- nymy in WordNet (Fellbaum, 1998). Likewise, most knowledge extraction systems focus on extracting one specific kind of knowledge from text, often fac- tual relationships (Banko et al., 2007; Suchanek et al., 2007; Wu and Weld, 2007), although other spe- cialized extraction techniques exist as well. 1 http://www.freebase.com/ If we continue to build knowledge collections fo- cused on specific types, will we collect a sufficient store of common sense knowledge for understand- ing language? What kinds of knowledge might lie outside the collections that the community has fo- cused on building? We have undertaken an empir- ical study of a natural language understanding task in order to help answer these questions. We focus on the Recognizing Textual Entailment (RTE) task (Dagan et al., 2006), which is the task of recogniz- ing whether the meaning of one text, called the Hy- pothesis (H), can be inferred from another, called the Text (T). With the help of five annotators, we have investigated the RTE-5 corpus to determine the types of knowledge involved in human judgments of RTE. We found 20 distinct categories of common- sense knowledge that featured prominently in RTE, besides linguistic knowledge, hyponymy, and syn- onymy. Inter-annotator agreement statistics indicate that these categories are well-defined. Many of the categories fall outside of the realm of all but the most general knowledge bases, like Cyc, and differ from the standard relational knowledge that most auto- mated knowledge extraction techniques try to find. The next section outlines the methodology of our
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2011-acl-world-knowledge-for-rte - Types of Common-Sense...

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