Extended probabilistic HAL with close temporal association for psychiatric query document retrieval

ABSTRACT

Psychiatric query document retrieval can assist individuals to locate query documents relevant to their depression-related problems efficiently and effectively. By referring to relevant documents, individuals can understand how to alleviate their depression-related symptoms according to recommendations from health professionals. This work presents an extended probabilistic Hyperspace Analog to Language (epHAL) model to achieve this aim. The epHAL incorporates the close temporal associations between words in query documents to represent word cooccurrence relationships in a high-dimensional context space. The information flow mechanism further combines the query words in the epHAL space to infer related words for effective information retrieval. The language model perplexity is considered as the criterion for model optimization. Finally, the epHAL is adopted for psychiatric query document retrieval, and indicates its superiority in information retrieval over traditional approaches.

REFERENCES

• 1: Abasolo, J. M. and Gómez, M. 2000. An ontology-based agent for information retrieval in medicine. In Proceedings of the 1st International Workshop on the Semantic Web. 73--82.
• 2: Juan M. Ale , Gustavo H. Rossi, An approach to discovering temporal association rules, Proceedings of the 2000 ACM symposium on Applied computing, p.294-300, March 2000, Como, Italy [doi>10.1145/335603.335770]
• 3: Gianni Amati , Cornelis Joost Van Rijsbergen, Probabilistic models of information retrieval based on measuring the divergence from randomness, ACM Transactions on Information Systems (TOIS), v.20 n.4, p.357-389, October 2002 [doi>10.1145/582415.582416]
• 4: Anagnostakis, A. G., Tzima, M., Sakellaris, G. C., Fotiadis, D. I., and Likas, A. C. 2005. Semantics-based information modeling for the health-care administration sector: The citation platform. IEEE Trans. Inform. Tech. Biomed. 9, 2, 239--247.
• 5: Leif Azzopardi , Mark Girolami , Malcolm Crowe, Probabilistic hyperspace analogue to language, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076135]
• 6: Bai, Y. M., Lin, C. C., Chen, J. Y., and Liu, W. C. 2001. Virtual psychiatric clinics. Am. J. Psychiat. 158, 7, 1160--1161.
• 7: Jing Bai , Dawei Song , Peter Bruza , Jian-Yun Nie , Guihong Cao, Query expansion using term relationships in language models for information retrieval, Proceedings of the 14th ACM international conference on Information and knowledge management, October 31-November 05, 2005, Bremen, Germany [doi>10.1145/1099554.1099725]
• 8: Jon Barwise , Jerry Seligman, Information flow: the logic of distributed systems, Cambridge University Press, New York, NY, 1997
• 9: Ricardo A. Baeza-Yates , Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
• 10: Burgess, C., Livesay, K., and Lund, K. 1998. Explorations in context space: Words, sentences, discourse. Disc. Proc. 25, 2&3, 211--257.
• 11: P. D. Bruza , D. Song, Inferring query models by computing information flow, Proceedings of the eleventh international conference on Information and knowledge management, November 04-09, 2002, McLean, Virginia, USA [doi>10.1145/584792.584837]
• 12: Guihong Cao , Jian-Yun Nie , Jing Bai, Integrating word relationships into language models, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076086]
• 13: Claudio Carpineto , Renato de Mori , Giovanni Romano , Brigitte Bigi, An information-theoretic approach to automatic query expansion, ACM Transactions on Information Systems (TOIS), v.19 n.1, p.1-27, Jan. 2001 [doi>10.1145/366836.366860]
• 14: Chen, S. F. and Goodman, J. 1998. An empirical study of smoothing techniques for language modeling. Tech. rep. TR--10--98. Center for Research in Computing Technology, Harvard University, Cambridge, MA.
• 15: CNS 14366. 1999. Segmentation principles for chinese literature processing. Bureau of Standards, Metrology & Inspection, M.O.E.A., Republic of China.
• 16: Tatiana Almeida Souza Coelho , Pável Pereira Calado , Lamarque Vieira Souza , Berthier Ribeiro-Neto , Richard Muntz, Image Retrieval Using Multiple Evidence Ranking, IEEE Transactions on Knowledge and Data Engineering, v.16 n.4, p.408-417, April 2004 [doi>10.1109/TKDE.2004.1269666]
• 17: Fellbaum, C. 1998. WordNet: An Electronic Lexical Database. MIT Press, Cambridge, MA.
• 18: Gardenfors, P. 2004. Conceptual Spaces: The Geometry of Thought. MIT Press, Cambridge, MA.
• 19: Huang, C. R., Gao, Z. M., Shen, C. C. C., and Chen, K. J. 1998. Quantitative criteria for computational Chinese lexicography: A study based on a Standard Reference Lexicon for Chinese NLP. Proceedings of the ROCLING XI. 87--108.
• 20: Xuedong Huang , Alex Acero , Raj Reddy , Hsiao-Wuen Hon, Spoken Language Processing: A Guide to Theory, Algorithm, and System Development, Prentice Hall PTR, Upper Saddle River, NJ, 2001
• 21: John Lafferty , Chengxiang Zhai, Document language models, query models, and risk minimization for information retrieval, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.111-119, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.383970]
• 22: Leroy, G. and Chen, H. 2001. Meeting medical terminology needs—the Ontology-Enhanced Medical Concept Mapper. IEEE Trans. Inform. Tech. Biomed. 5, 4, 261--270.
• 23: Li, D., Lo, W. K., and Meng, H. 2006. Initial Experiments on Automatic Story Segmentation in Chinese Spoken Documents using Lexical Cohesion of Extracted Named Entities. Lecture Notes in Artificial Intelligence, vol. 4724. Springer, Berlin, Germany, 693--703.
• 24: Jimmy Lin , Dina Demner-Fushman, The role of knowledge in conceptual retrieval: a study in the domain of clinical medicine, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148191]
• 25: Lindberg, D. A., Humphreys, B. L., and McCray, A. T. 1993. The unified medical language system. Meth. Inform. Med. 32, 281--291.
• 26: Shuang Liu , Fang Liu , Clement Yu , Weiyi Meng, An effective approach to document retrieval via utilizing WordNet and recognizing phrases, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009039]
• 27: Christopher D. Manning , Hinrich Schütze, Foundations of statistical natural language processing, MIT Press, Cambridge, MA, 1999
• 28: Sven Martin , Jörg Liermann , Hermann Ney, Algorithms for bigram and trigram word clustering, Speech Communication, v.24 n.1, p.19-37, April 1, 1998 [doi>10.1016/S0167-6393(97)00062-9]
• 29: Jian-Yun Nie , Guihong Cao , Jing Bai, Inferential language models for information retrieval, ACM Transactions on Asian Language Information Processing (TALIP), v.5 n.4, p.296-322, December 2006 [doi>10.1145/1236181.1236183]
• 30: Jay M. Ponte , W. Bruce Croft, A language modeling approach to information retrieval, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, p.275-281, August 24-28, 1998, Melbourne, Australia [doi>10.1145/290941.291008]
• 31: Rosenfeld, R. 1996. A maximum entropy approach to adaptive statistical language modeling. Comput. Speech Lang. 10, 187--228.
• 32: Gerard Salton , Michael J. McGill, Introduction to Modern Information Retrieval, McGraw-Hill, Inc., New York, NY, 1986
• 33: Gerard Salton , Christopher Buckley, Term-weighting approaches in automatic text retrieval, Information Processing and Management: an International Journal, v.24 n.5, p.513-523, 1988 [doi>10.1016/0306-4573(88)90021-0]
• 34: Salton, G. and Buckley, C. 1990. Improving retrieval performance by relevance feedback. J. Amer. Soc. Inform. Sci. Tech. 41, 4, 288--297.
• 35: Shannon, C. 1948. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379--423.
• 36: Dawei Song , Peter Bruza, Discovering information flow suing high dimensional conceptual space, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.327-333, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.384017]
• 37: D. Song , P. D. Bruza, Towards context sensitive information inference, Journal of the American Society for Information Science and Technology, v.54 n.4, p.321-334, February 15, 2003 [doi>10.1002/asi.10213]
• 38: Vishwa Vinay , Ingemar J. Cox , Natasa Milic-Frayling , Ken Wood, Can constrained relevance feedback and display strategies help users retrieve items on mobile devices?, Information Retrieval, v.9 n.4, p.435-453, September 2006 [doi>10.1007/s10791-006-6391-7]
• 39: Chung-Hsien Wu , Liang-Chih Yu , Fong-Lin Jang, Using Semantic Dependencies to Mine Depressive Symptoms from Consultation Records, IEEE Intelligent Systems, v.20 n.6, p.50-58, November 2005 [doi>10.1109/MIS.2005.115]
• 40: Chung-Hsien Wu , Jui-Feng Yeh , Ming-Jun Chen, Domain-specific FAQ retrieval using independent aspects, ACM Transactions on Asian Language Information Processing (TALIP), v.4 n.1, p.1-17, March 2005 [doi>10.1145/1066078.1066079]
• 41: Chung-Hsien Wu , Jui-Feng Yeh , Yu-Sheng Lai, Semantic Segment Extraction and Matching for Internet FAQ Retrieval, IEEE Transactions on Knowledge and Data Engineering, v.18 n.7, p.930-940, July 2006 [doi>10.1109/TKDE.2006.115]
• 42: Yu, L. C., Wu, C. H., and Jang, F. L. 2007. Psychiatric consultation record retrieval using scenario-based representation and multilevel mixture model. IEEE Trans. Inform. Tech. Biomed. 11, 4, 415--427.
• 43: Chengxiang Zhai , John Lafferty, A study of smoothing methods for language models applied to information retrieval, ACM Transactions on Information Systems (TOIS), v.22 n.2, p.179-214, April 2004 [doi>10.1145/984321.984322]

INDEX TERMS

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

Additional Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.2 Design Methodology
          Subjects: Classifier design and evaluation

  J. Computer Applications
  J.3 LIFE AND MEDICAL SCIENCES
      Subjects: Medical information systems

General Terms:
Algorithms, Experimentation, Theory

Keywords:
Hyperspace Analog to Language (HAL) model, Information retrieval, information flow, query documents