Academic conference homepage understanding using constrained hierarchical conditional random fields

ABSTRACT

We address the problem of academic conference homepage understanding for the Semantic Web. This problem consists of three labeling tasks - labeling conference function pages, function blocks, and attributes. Different from traditional information extraction tasks, the data in academic conference homepages has complex structural dependencies across multiple Web pages. In addition, there are logical constraints in the data. In this paper, we propose a unified approach, Constrained Hierarchical Conditional Random Fields, to accomplish the three labeling tasks simultaneously. In this approach, complex structural dependencies can be well described. Also, the constrained Viterbi algorithm in the inference process can avoid logical errors. Experimental results on real world conference data have demonstrated that this approach performs better than cascaded labeling methods by 3.6% in F1-measure and that the constrained inference process can improve the accuracy by 14.3%. Based on the proposed approach, we develop a prototype system of use-oriented semantic academic conference calendar. The user simply needs to specify what conferences he/she is interested in. Subsequently, the system finds, extracts, and updates the semantic information from the Web, and then builds a calendar automatically for the user. The semantic conference data can be used in other applications, such as finding sponsors and finding experts. The proposed approach can be used in other information extraction tasks as well.

REFERENCES

• 1: Auer, S., Dietzold, S., and Riechert, T. OntoWiki - A Tool for Social, Semantic Collaboration. In Proc. of ISWC, 2006.
• 2: Deng Cai , Shipeng Yu , Ji-Rong Wen , Wei-Ying Ma, Block-based web search, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009070]
• 3: Ciravegna, F. (LP)² An Adaptive Algorithm for Information Extraction from Web-related Texts. In Proc. of the IJCAI-2001 Workshop on Adaptive Text Extraction and Mining, Seattle, USA, 2001.
• 4: Ciravegna, F., Dingli, A., Iria, J., and Wilks, Y. Multi-strategy Definition of Annotation Services in Melita, In Proc. of ISWC'2003 Workshop on Human Language Technology for the Semantic Web and Web Services, 2003, 97--107.
• 5: Michael Collins, Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms, Proceedings of the ACL-02 conference on Empirical methods in natural language processing, p.1-8, July 06, 2002 [doi>10.3115/1118693.1118694]
• 6: Corinna Cortes , Vladimir Vapnik, Support-Vector Networks, Machine Learning, v.20 n.3, p.273-297, Sept. 1995 [doi>10.1023/A:1022627411411]
• 7: Robert G. Cowell , Steffen L. Lauritzen , A. Philip David , David J. Spiegelhalter , V. Nair , J. Lawless , M. Jordan, Probabilistic Networks and Expert Systems, Springer-Verlag New York, Inc., Secaucus, NJ, 1999
• 8: Cox, C., Nicolson, J., Finkel, J., and Manning, C. Template Sampling for Leveraging Domain Knowledge in Information Extraction. In PASCAL Challenges, 2005.
• 9: Gandon, F., and Sadeh, N. A Semantic eWallet to Reconcile Privacy and Context Awareness. In Proc. of ISWC, 2003.
• 10: Zoubin Ghahramani , Michael I. Jordan, Factorial Hidden Markov Models, Machine Learning, v.29 n.2-3, p.245-273, Nov./Dec. 1997 [doi>10.1023/A:1007425814087]
• 11: Hammersley, J. and Clifford, P. Markov fields on Finite Graphs and Lattices. 1971.
• 12: He, X., Zemel, R., and Carreira-Perpiñán, M. Multiscale Conditional Random Fields for Image Labeling. In Proc of CVPR, 2004, 695--702.
• 13: Neil Ireson , Fabio Ciravegna , Mary Elaine Califf , Dayne Freitag , Nicholas Kushmerick , Alberto Lavelli, Evaluating machine learning for information extraction, Proceedings of the 22nd international conference on Machine learning, p.345-352, August 07-11, 2005, Bonn, Germany [doi>10.1145/1102351.1102395]
• 14: Kristjansson, T., Culotta, A., Viola, P., and McCallum, A. Interactive Information Extraction with Constrained Condition Random Fields. In Proc of AAAI, 2004, 412--418.
• 15: John D. Lafferty , Andrew McCallum , Fernando C. N. Pereira, Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, Proceedings of the Eighteenth International Conference on Machine Learning, p.282-289, June 28-July 01, 2001
• 16: Lazarinis, F. Combining Information Retrieval with Information Extraction for Efficient Retrieval of Calls for Papers. In Proc. of IRSG, 1998.
• 17: Li, Y., Bontcheva, K., and Cunningham, H. Using Uneven Margins SVM and Perceptron for Information Extraction. In Proc. of CoNLL, 2005.
• 18: D. C. Liu , J. Nocedal, On the limited memory BFGS method for large scale optimization, Mathematical Programming: Series A and B, v.45 n.3, p.503-528, Dec. 1989 [doi>10.1007/BF01589116]
• 19: Andrew McCallum , Dayne Freitag , Fernando C. N. Pereira, Maximum Entropy Markov Models for Information Extraction and Segmentation, Proceedings of the Seventeenth International Conference on Machine Learning, p.591-598, June 29-July 02, 2000
• 20: Terry R. Payne , Rahul Singh , Katia P. Sycara, Browsing Schedules - An Agent-Based Approach to Navigating the Semantic Web, Proceedings of the First International Semantic Web Conference on The Semantic Web, p.469-474, June 09-12, 2002
• 21: C. J. Van Rijsbergen, Information Retrieval, Butterworth-Heinemann, Newton, MA, 1979
• 22: Dan Roth , Wen-tau Yih, Integer linear programming inference for conditional random fields, Proceedings of the 22nd international conference on Machine learning, p.736-743, August 07-11, 2005, Bonn, Germany [doi>10.1145/1102351.1102444]
• 23: Sarawagi, S. and Cohen, W. Semi-markov Conditional Random Fields for Information Extraction. In Proc. of NIPS, 2004.
• 24: Schneider, K. Information Extraction from Calls for Papers with Conditional Random Fields and Layout Features. In Proc. of AICS, 2005, 267--276.
• 25: Fei Sha , Fernando Pereira, Shallow parsing with conditional random fields, Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, p.134-141, May 27-June 01, 2003, Edmonton, Canada [doi>10.3115/1073445.1073473]
• 26: Tang, J., Hong, M., Li, J., and Liang, B. Tree-structured Conditional Random Fields for Semantic Annotation. In Proc. of ISWC, 2006, 640--653.
• 27: Yedidia, J., Freeman, W., and Weiss, Y. Generalized Belief Propagation. In Proc. of NIPS, 2000.
• 28: Jun Zhu , Zaiqing Nie , Ji-Rong Wen , Bo Zhang , Wei-Ying Ma, 2D Conditional Random Fields for Web information extraction, Proceedings of the 22nd international conference on Machine learning, p.1044-1051, August 07-11, 2005, Bonn, Germany [doi>10.1145/1102351.1102483]
• 29: Jun Zhu , Zaiqing Nie , Ji-Rong Wen , Bo Zhang , Wei-Ying Ma, Simultaneous record detection and attribute labeling in web data extraction, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA [doi>10.1145/1150402.1150457]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.1 Models
          Subjects: Statistical

General Terms:
Algorithms, Experimentation

Keywords:
constrained hierarchical conditional random fields, information extraction, semantic conference information