A Generalized Framework for Revealing Analogous Themes across Related Topics

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 979–986, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics A Generalized Framework for Revealing Analogous Themes across Related Topics Zvika Marx Ido Dagan Eli Shamir CS and AI Laboratory Computer Science Department School of Computer Science MIT Bar-Ilan University The Hebrew University Cambridge, MA 02139, US Ramat-Gan 52900, Israel Jerusalem 91904, Israel zvim@csail.mit.edu dagan@cs.biu.ac.il shamir@cs.huji.ac.il Abstract This work addresses the task of identify- ing thematic correspondences across sub- corpora focused on different topics. We introduce an unsupervised algorithmic framework based on distributional data clustering, which generalizes previous ini- tial works on this task. The empirical re- sults reveal interesting commonalities of different religions. We evaluate the re- sults through measuring the overlap of our clusters with clusters compiled manually by experts. The tested variants of our framework are shown to outperform al- ternative methods applicable to the task. 1 Introduction The ability to identify analogies and correspon- dences is one of the fascinating aspects of intelli- gence. Research in cognitive science has acknowledged the significance of this ability of human thinking, particularly in learning across dif- ferent situations or domains where the common base to learning is not straightforward. Several previous computational models of analogy making (e.g. Falkenhainer et al., 1989) suggested symbolic computational mechanisms for constructing de- tailed mappings that connect corresponding ingre- dients across analogized systems. This work explores the identification of thematic correspondences in texts through an extension of the well known data clustering problem. Previous works aimed at identifying – through clusters of words – concepts, sub-topics or themes that are prominent within a corpus of texts (e.g., Pereira et al., 1993; Li, 2002; Lin and Pantel, 2002). The current work deals with extending this line of re- search to identify corresponding themes across a corpus pre-divided to several sub-corpora, which are focused on different, yet related, topics. This research task has been defined quite re- cently (Dagan et al., 2002), and has not been ex- plored extensively yet. One could think, however, of many potential applications for drawing corre- spondences across textual resources: comparison of related firms or products, identifying equivalen- cies in news published in different countries, and so on. The experimental part of our work deals with revealing correspondences between different religions: Buddhism, Christianity, Hinduism, Islam and Judaism. Given a pre-partition of the corpus to sub-corpora, one for each religion, our method ex- poses common aspects for all religions, such as sacred writings, festivals and suffering. The mechanism we employ directs correspond- ing key terms in the different sub-corpora, such as names of festivals of different religions, to be in- cluded in the same cluster. Term clustering meth- ods in general, and in this work in particular, rely on word co-occurrence statistics: terms sharing similar words co-occurrence statistics are clustered together. Different topics, however, are character- ized by distinctive terminology and typical word co-locations. Therefore, given a pre-divided cor- pus, similar co-occurrence patterns would typically be …

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