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We present a web mining method for discovering 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 previous work. Our method is based on clustering 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 precision. . | 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 discovering 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 previous work. Our method is based on clustering 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 precision. 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 providing 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 c p loit d to automatically generate 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 observed that given one or more such lexical patterns a corpus could be used to generate examples of hyponyms that could then in turn be exploited to gen-232 erate more lexical patterns. The larger and more reliable 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 .
