Cross-dataset Clustering: Revealing Corresponding Themes across Multiple Corpora

Cross-dataset Clustering: Revealing Corresponding Themes Across Multiple Corpora Ido DAGAN Department of Computer Science Bar-Ilan University Ramat-Gan, Israel, 52900 and LingoMotors Inc. dagan@lingomotors.com Zvika MARX Center for Neural Computation The Hebrew University and CS Dept., Bar-Ilan University Ramat-Gan, Israel, 52900 zvim@cs.huji.ac.il Eli SHAMIR School of Computer Science and Engineering The Hebrew University Jerusalem, Israel, 91904 shamir@cs.huji.ac.il Abstract We present a method for identifying corresponding themes across several corpora that are focused on related, but distinct, domains. This task is approached through simultaneous clustering of keyword sets extracted from the analyzed corpora. Our algorithm extends the information- bottleneck soft clustering method for a suitable setting consisting of several datasets. Experimentation with topical corpora reveals similar aspects of three distinct religions. The evaluation is by way of comparison to clusters constructed manually by an expert. 1 Introduction This paper addresses the problem of detecting corresponding subtopics, or themes, within related bodies of text. Such task is typical to comparative research, whether commercial or scientific: a conceivable application would aim at detecting corresponding characteristics regarding, e.g., companies, markets, legal systems or political organizations. Clustering has often been perceived as a mean for extracting meaningful components from data (Tishby, Pereira and Bialek, 1999). Regarding textual data, clusters of words (Pereira, Tishby and Lee, 1993) or documents (Lee and Seung, 1999; Dhillon and Modha, 2001) are often interpreted as capturing topics or themes that play prominent role in the analyzed texts. Our work extends the “standard” clustering paradigm, which pertains to a single dataset. We address a setting in which several datasets, corresponding to related domains, are given. We focus on the comparative task of detecting those themes that are expressed across several datasets, rather than discovering internal themes within each individual dataset. More specifically, we address the task of clustering simultaneously multiple datasets such that each cluster includes elements from several datasets, capturing a common theme, which is shared across the sets. We term this task cross- dataset (CD) clustering. In this article we demonstrate CD clustering through detecting corresponding themes across three different religions. That is: we apply our approach to three sets of religion-related keywords, extracted from three corpora, which include encyclopedic entries and introductory articles regarding Buddhism, Christianity and Islam. Each one of three representative keyword-sets, which were extracted from the above corpora, presumably encapsulates topics and themes discussed within its source corpus. Our algorithm succeeds to reveal common themes such as scriptures, rituals and schools through respective keyword clusters consisting of terms such as Sutra, Bible and Quran; Full Moon, Easter and Id al Fitr; Theravada, Protestant and Shiite (see Table 1 below for a detailed depiction of our results). The CD clustering algorithm, presented in Section 2.2 below, extends the information bottleneck (IB) soft clustering method. Our modifications to the IB formulation enable the clustering algorithm to capture characteristic patterns that run across different datasets, rather then being “trapped” by unique characteristics of individual datasets. Like other topic discovery tasks that are approached by clustering, the goal of CD …

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