Mining multi-faceted overviews of arbitrary topics in a text collection

ABSTRACT

A common task in many text mining applications is to generate a multi-faceted overview of a topic in a text collection. Such an overview not only directly serves as an informative summary of the topic, but also provides a detailed view of navigation to different facets of the topic. Existing work has cast this problem as a categorization problem and requires training examples for each facet. This has three limitations: (1) All facets are predefined, which may not fit the need of a particular user. (2) Training examples for each facet are often unavailable. (3) Such an approach only works for a predefined type of topics. In this paper, we break these limitations and study a more realistic new setup of the problem, in which we would allow a user to flexibly describe each facet with keywords for an arbitrary topic and attempt to mine a multi-faceted overview in an unsupervised way. We attempt a probabilistic approach to solve this problem. Empirical experiments on different genres of text data show that our approach can effectively generate a multi-faceted overview for arbitrary topics; the generated overviews are comparable with those generated by supervised methods with training examples. They are also more informative than unstructured flat summaries. The method is quite general, thus can be applied to multiple text mining tasks in different application domains.

REFERENCES

• 1: David M. Blei , Andrew Y. Ng , Michael I. Jordan, Latent dirichlet allocation, The Journal of Machine Learning Research, 3, p.993-1022, 3/1/2003 [doi>10.1162/jmlr.2003.3.4-5.993]
• 2: Hao Chen , Susan Dumais, Bringing order to the Web: automatically categorizing search results, Proceedings of the SIGCHI conference on Human factors in computing systems, p.145-152, April 01-06, 2000, The Hague, The Netherlands [doi>10.1145/332040.332418]
• 3: Kenneth Ward Church , Patrick Hanks, Word association norms, mutual information, and lexicography, Computational Linguistics, v.16 n.1, p.22-29, March 1990
• 4: Susan Dumais , Edward Cutrell , Hao Chen, Optimizing search by showing results in context, Proceedings of the SIGCHI conference on Human factors in computing systems, p.277-284, March 2001, Seattle, Washington, United States [doi>10.1145/365024.365116]
• 5: Daniel Gruhl , R. Guha , Ravi Kumar , Jasmine Novak , Andrew Tomkins, The predictive power of online chatter, Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, August 21-24, 2005, Chicago, Illinois, USA [doi>10.1145/1081870.1081883]
• 6: Daniel Gruhl , R. Guha , David Liben-Nowell , Andrew Tomkins, Information diffusion through blogspace, Proceedings of the 13th international conference on World Wide Web, May 17-20, 2004, New York, NY, USA [doi>10.1145/988672.988739]
• 7: Marti A. Hearst , Jan O. Pedersen, Reexamining the cluster hypothesis: scatter/gather on retrieval results, Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, p.76-84, August 18-22, 1996, Zurich, Switzerland [doi>10.1145/243199.243216]
• 8: Thomas Hofmann, Probabilistic latent semantic indexing, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, p.50-57, August 15-19, 1999, Berkeley, California, United States [doi>10.1145/312624.312649]
• 9: Jing Jiang , ChengXiang Zhai, Exploiting domain structure for named entity recognition, Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, p.74-81, June 04-09, 2006, New York, New York [doi>10.3115/1220835.1220845]
• 10: Kullback, S. and Leibler, R. A. On information and sufficiency. The Annals of Mathematical Statistics, 22(1):79--86, mar 1951.
• 11: X. Ling, J. Jiang, X. He, Q. Mei, C. Zhai, and B. Schatz. Automatically generating gene summaries from biomedical literature. In Proceedings of PSB '06, pages 41--50, 2006.
• 12: Xu Ling , Jing Jiang , Xin He , Qiaozhu Mei , Chengxiang Zhai , Bruce Schatz, Generating gene summaries from biomedical literature: A study of semi-structured summarization, Information Processing and Management: an International Journal, v.43 n.6, p.1777-1791, November, 2007 [doi>10.1016/j.ipm.2007.01.018]
• 13: Bing Liu , Minqing Hu , Junsheng Cheng, Opinion observer: analyzing and comparing opinions on the Web, Proceedings of the 14th international conference on World Wide Web, May 10-14, 2005, Chiba, Japan [doi>10.1145/1060745.1060797]
• 14: Yue Lu , Chengxiang Zhai, Opinion integration through semi-supervised topic modeling, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China [doi>10.1145/1367497.1367514]
• 15: G. J. McLachlan and T. Krishnan. The EM Algorithm and Extensions. Wiley, 1997.
• 16: Qiaozhu Mei , Deng Cai , Duo Zhang , ChengXiang Zhai, Topic modeling with network regularization, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China [doi>10.1145/1367497.1367512]
• 17: Qiaozhu Mei , Xu Ling , Matthew Wondra , Hang Su , ChengXiang Zhai, Topic sentiment mixture: modeling facets and opinions in weblogs, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada [doi>10.1145/1242572.1242596]
• 18: Qiaozhu Mei , ChengXiang Zhai, A mixture model for contextual text mining, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA [doi>10.1145/1150402.1150482]
• 19: R. M. Neal and G. E. Hinton. A view of the em algorithm that justifies incremental, sparse, and other variants. pages 355--368, 1999.
• 20: Peter Pirolli , Patricia Schank , Marti Hearst , Christine Diehl, Scatter/gather browsing communicates the topic structure of a very large text collection, Proceedings of the SIGCHI conference on Human factors in computing systems: common ground, p.213-220, April 13-18, 1996, Vancouver, British Columbia, Canada [doi>10.1145/238386.238489]
• 21: M. A. C. R. A. Drysdale and T. F. Consortium. Flybase: genes and gene models. Nucleic Acids Res., 33:390--395, 2005.
• 22: E. Stoica, M. Hearst, and M. Richardson. Automating creation of hierarchical faceted metadata structures. In Proceedings of NAACL/HLT '2007, pages 244--251, 2007.
• 23: C. J. Van Rijsbergen, Information Retrieval, Butterworth-Heinemann, Newton, MA, 1979
• 24: Xuanhui Wang , ChengXiang Zhai, Learn from web search logs to organize search results, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277759]
• 25: Oren Zamir , Oren Etzioni, Grouper: a dynamic clustering interface to Web search results, Computer Networks: The International Journal of Computer and Telecommunications Networking, v.31 n.11-16, p.1361-1374, May 17, 1999
• 26: Hua-Jun Zeng , Qi-Cai He , Zheng Chen , Wei-Ying Ma , Jinwen Ma, Learning to cluster web search results, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009030]
• 27: ChengXiang Zhai , Atulya Velivelli , Bei Yu, A cross-collection mixture model for comparative text mining, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA [doi>10.1145/1014052.1014150]
• 28: X. Zhu, Z. Ghahramani, and J. D. Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. In ICML, pages 912--919, 2003.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Algorithms

Keywords:
multi-faceted overview, statistical topic models, text mining