Large-Scale Noun Compound Interpretation Using Bootstrapping and the Web as a Corpus

Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 648–658, Edinburgh, Scotland, UK, July 27–31, 2011. c⃝2011 Association for Computational Linguistics Large-Scale Noun Compound Interpretation Using Bootstrapping and the Web as a Corpus Su Nam Kim Computer Science & Software Engineering University of Melbourne Melbourne, VIC 3010 Australia snkim@csse.unimelb.edu.au Preslav Nakov Department of Computer Science National University of Singapore 13 Computing Drive Singapore 117417 nakov@comp.nus.edu.sg Abstract Responding to the need for semantic lexical resources in natural language processing ap- plications, we examine methods to acquire noun compounds (NCs), e.g., orange juice, to- gether with suitable fine-grained semantic in- terpretations, e.g., squeezed from, which are directly usable as paraphrases. We employ bootstrapping and web statistics, and utilize the relationship between NCs and paraphras- ing patterns to jointly extract NCs and such patterns in multiple alternating iterations. In evaluation, we found that having one com- pound noun fixed yields both a higher number of semantically interpreted NCs and improved accuracy due to stronger semantic restrictions. 1 Introduction Noun compounds (NCs) such as malaria mosquito and colon cancer tumor suppressor protein are chal- lenging for text processing since the relationship between the nouns they are composed of is im- plicit. NCs are abundant in English and understand- ing their semantics is important in many natural lan- guage processing (NLP) applications. For example, a question answering system might need to know whether protein acting as a tumor suppressor is a good paraphrase for tumor suppressor protein. Sim- ilarly, a machine translation system facing the un- known noun compound Geneva headquarters might translate it better if it could first paraphrase it as Geneva headquarters of the WTO. Given a query for “migraine treatment”, an information retrieval system could use paraphrasing verbs like relieve and prevent for query expansion and result ranking. Most work on noun compound interpretation has focused on two-word NCs. There have been two general lines of research: the first one derives the NC semantics from the semantics of the nouns it is made of (Rosario and Hearst, 2002; Moldovan et al., 2004; Kim and Baldwin, 2005; Girju, 2007; S´eaghdha, 2009; Tratz and Hovy, 2010), while the second one models the relationship between the nouns directly (Vanderwende, 1994; Lapata, 2002; Kim and Bald- win, 2006; Nakov and Hearst, 2006; Nakov and Hearst, 2008; Butnariu and Veale, 2008). In either case, the semantics of an NC is typi- cally expressed by an abstract relation like CAUSE (e.g., malaria mosquito), SOURCE (e.g., olive oil), or PURPOSE (e.g., migraine drug), coming from a small fixed inventory. Some researchers however, have argued for a more fine-grained, even infinite, inventory (Finin, 1980). Verbs are particularly use- ful in this respect and can capture elements of the semantics that the abstract relations cannot. For ex- ample, while most NCs expressing MAKE, can be paraphrased by common patterns like be made of and be composed of, some NCs allow more specific patterns, e.g., be squeezed from for orange juice, and be topped with for bacon pizza. …

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