Fully Unsupervised Discovery of Concept-Specific Relationships by Web Mining

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 232–239, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Fully Unsupervised Discovery of Concept-Specific Relationships by Web Mining Dmitry Davidov ICNC The Hebrew University Jerusalem 91904, Israel dmitry@alice.nc.huji.ac.il Ari Rappoport Institute of Computer Science The Hebrew University Jerusalem 91904, Israel www.cs.huji.ac.il/∼arir Moshe Koppel Dept. of Computer Science Bar-Ilan University Ramat-Gan 52900, Israel koppel@cs.biu.ac.il Abstract We present a web mining method for discov- ering and enhancing relationships in which a specified concept (word class) participates. We discover a whole range of relationships focused on the given concept, rather than generic known relationships as in most pre- vious work. Our method is based on cluster- ing patterns that contain concept words and other words related to them. We evaluate the method on three different rich concepts and find that in each case the method generates a broad variety of relationships with good pre- cision. 1 Introduction The huge amount of information available on the web has led to a flurry of research on methods for automatic creation of structured information from large unstructured text corpora. The challenge is to create as much information as possible while pro- viding as little input as possible. A lot of this research is based on the initial insight (Hearst, 1992) that certain lexical patterns (‘X is a country’) can be exploited to automatically gener- ate hyponyms of a specified word. Subsequent work (to be discussed in detail below) extended this initial idea along two dimensions. One objective was to require as small a user- provided initial seed as possible. Thus, it was ob- served that given one or more such lexical patterns, a corpus could be used to generate examples of hy- ponyms that could then, in turn, be exploited to gen- erate more lexical patterns. The larger and more reli- able sets of patterns thus generated resulted in larger and more precise sets of hyponyms and vice versa. The initial step of the resulting alternating bootstrap process – the user-provided input – could just as well consist of examples of hyponyms as of lexical pat- terns. A second objective was to extend the information that could be learned from the process beyond hy- ponyms of a given word. Thus, the approach was extended to finding lexical patterns that could pro- duce synonyms and other standard lexical relations. These relations comprise all those words that stand in some known binary relation with a specified word. In this paper, we introduce a novel extension of this problem: given a particular concept (initially represented by two seed words), discover relations in which it participates, without specifying their types in advance. We will generate a concept class and a variety of natural binary relations involving that class. An advantage of our method is that it is particu- larly suitable for web mining, even given the restric- tions on query amounts that exist in some of today’s leading search engines. The outline of the paper …

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