Which Noun Phrases Denote Which Concepts?

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 570–580, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Which Noun Phrases Denote Which Concepts? Jayant Krishnamurthy Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 jayantk@cs.cmu.edu Tom M. Mitchell Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 tom.mitchell@cmu.edu Abstract Resolving polysemy and synonymy is re- quired for high-quality information extraction. We present ConceptResolver, a component for the Never-Ending Language Learner (NELL) (Carlson et al., 2010) that handles both phe- nomena by identifying the latent concepts that noun phrases refer to. ConceptResolver per- forms both word sense induction and synonym resolution on relations extracted from text us- ing an ontology and a small amount of la- beled data. Domain knowledge (the ontology) guides concept creation by defining a set of possible semantic types for concepts. Word sense induction is performed by inferring a set of semantic types for each noun phrase. Syn- onym detection exploits redundant informa- tion to train several domain-specific synonym classifiers in a semi-supervised fashion. When ConceptResolver is run on NELL’s knowledge base, 87% of the word senses it creates cor- respond to real-world concepts, and 85% of noun phrases that it suggests refer to the same concept are indeed synonyms. 1 Introduction Many information extraction systems construct knowledge bases by extracting structured assertions from free text (e.g., NELL (Carlson et al., 2010), TextRunner (Banko et al., 2007)). A major limi- tation of many of these systems is that they fail to distinguish between noun phrases and the underly- ing concepts they refer to. As a result, a polysemous phrase like “apple” will refer sometimes to the con- cept Apple Computer (the company), and other times to the concept apple (the fruit). Furthermore, two synonymous noun phrases like “apple” and “Apple “apple” “apple computer” apple (the fruit) Apple Computer Figure 1: An example mapping from noun phrases (left) to a set of underlying concepts (right). Arrows indicate which noun phrases can refer to which concepts. [eli lilly, lilly] [kaspersky labs, kaspersky lab, kaspersky] [careerbuilder, careerbuilder.com] [l 3 communications, level 3 communications] [cellular, u.s. cellular] [jc penney, jc penny] [nielsen media research, nielsen company] [universal studios, universal music group, universal] [amr corporation, amr] [intel corp, intel corp., intel corporation, intel] [emmitt smith, chris canty] [albert pujols, pujols] [carlos boozer, dennis martinez] [jason hirsh, taylor buchholz] [chris snyder, ryan roberts] [j.p. losman, losman, jp losman] [san francisco giants, francisco rodriguez] [andruw jones, andruw] [aaron heilman, bret boone] [roberto clemente, clemente] Figure 2: A random sample of concepts created by Con- ceptResolver. The first 10 concepts are from company, while the second 10 are from athlete. Computer” can refer to the same underlying con- cept. The result of ignoring this many-to-many map- ping between noun phrases and underlying concepts (see Figure 1) is confusion about the meaning of ex- tracted information. To minimize such confusion, a system must separately represent noun phrases, the underlying concepts to which they can refer, and the many-to-many “can …

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