Deriving Generalized Knowledge from Corpora Using WordNet Abstraction

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 808–816, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Deriving Generalized Knowledge from Corpora using WordNet Abstraction Benjamin Van Durme, Phillip Michalak and Lenhart K. Schubert Department of Computer Science University of Rochester Rochester, NY 14627, USA Abstract Existing work in the extraction of com- monsense knowledge from text has been primarily restricted to factoids that serve as statements about what may possibly ob- tain in the world. We present an ap- proach to deriving stronger, more general claims by abstracting over large sets of factoids. Our goal is to coalesce the ob- served nominals for a given predicate ar- gument into a few predominant types, ob- tained as WordNet synsets. The results can be construed as generically quantified sen- tences restricting the semantic type of an argument position of a predicate. 1 Introduction Our interest is ultimately in building systems with commonsense reasoning and language un- derstanding abilities. As is widely appreciated, such systems will require large amounts of gen- eral world knowledge. Large text corpora are an attractive potential source of such knowledge. However, current natural language understand- ing (NLU) methods are not general and reliable enough to enable broad assimilation, in a formal- ized representation, of explicitly stated knowledge in encyclopedias or similar sources. As well, such sources typically do not cover the most obvious facts of the world, such as that ice cream may be delicious and may be coated with chocolate, or that children may play in parks. Methods currently exist for extracting simple “factoids” like those about ice cream and children just mentioned (see in particular (Schubert, 2002; Schubert and Tong, 2003)), but these are quite weak as general claims, and – being unconditional – are unsuitable for inference chaining. Consider however the fact that when something is said, it is generally said by a person, organization or text source; this a conditional statement dealing with the potential agents of saying, and could enable useful inferences. For example, in the sentence, “The tires were worn and they said I had to re- place them”, they might be mistakenly identified with the tires, without the knowledge that saying is something done primarily by persons, organiza- tions or text sources. Similarly, looking into the future one can imagine telling a household robot, “The cat needs to drink something”, with the ex- pectation that the robot will take into account that if a cat drinks something, it is usually water or milk (whereas people would often have broader options). The work reported here is aimed at deriving generalizations of the latter sort from large sets of weaker propositions, by examining the hierarchi- cal relations among sets of types that occur in the argument positions of verbal or other predicates. The generalizations we are aiming at are certainly not the only kinds derivable from text corpora (as the extensive literature on finding isa-relations, partonomic relations, paraphrase relations, etc. at- tests), …

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