Automatic Set Instance Extraction using the Web

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 441–449, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Automatic Set Instance Extraction using the Web Richard C. Wang Language Technologies Institute Carnegie Mellon University rcwang@cs.cmu.edu William W. Cohen Machine Learning Department Carnegie Mellon University wcohen@cs.cmu.edu Abstract An important and well-studied problem is the production of semantic lexicons from a large corpus. In this paper, we present a system named ASIA (Automatic Set In- stance Acquirer), which takes in the name of a semantic class as input (e.g., “car makers”) and automatically outputs its in- stances (e.g., “ford”, “nissan”, “toyota”). ASIA is based on recent advances in web- based set expansion - the problem of find- ing all instances of a set given a small number of “seed” instances. This ap- proach effectively exploits web resources and can be easily adapted to different languages. In brief, we use language- dependent hyponym patterns to find a noisy set of initial seeds, and then use a state-of-the-art language-independent set expansion system to expand these seeds. The proposed approach matches or outper- forms prior systems on several English- language benchmarks. It also shows ex- cellent performance on three dozen addi- tional benchmark problems from English, Chinese and Japanese, thus demonstrating language-independence. 1 Introduction An important and well-studied problem is the pro- duction of semantic lexicons for classes of in- terest; that is, the generation of all instances of a set (e.g., “apple”, “orange”, “banana”) given a name of that set (e.g., “fruits”). This task is often addressed by linguistically analyzing very large collections of text (Hearst, 1992; Kozareva et al., 2008; Etzioni et al., 2005; Pantel and Ravichandran, 2004; Pasca, 2004), often using hand-constructed or machine-learned shallow lin- guistic patterns to detect hyponym instances. A hy- ponym is a word or phrase whose semantic range Figure 1: Examples of SEAL’s input and output. English entities are reality TV shows, Chinese en- tities are popular Taiwanese foods, and Japanese entities are famous cartoon characters. is included within that of another word. For exam- ple, x is a hyponym of y if x is a (kind of) y. The opposite of hyponym is hypernym. In this paper, we evaluate a novel approach to this problem, embodied in a system called ASIA1 (Automatic Set Instance Acquirer). ASIA takes a semantic class name as input (e.g., “car makers”) and automatically outputs instances (e.g., “ford”, “nissan”, “toyota”). Unlike prior methods, ASIA makes heavy use of tools for web-based set ex- pansion. Set expansion is the task of finding all instances of a set given a small number of exam- ple (seed) instances. ASIA uses SEAL (Wang and Cohen, 2007), a language-independent web-based system that performed extremely well on a large number of benchmark sets – given three correct seeds, SEAL obtained average MAP scores in the high 90’s for 36 benchmark problems, including a dozen test problems each for English, Chinese and Japanese. SEAL works well in part because it …

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