Modelling citation networks for improving scientific paper classification performance

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4099 LNAI, Issue , 2006, Pages 413-422

  Zhang, Mengjie ^a,b   Gao, Xiaoying ^a,b   Cao, Minh Due ^a   Ma, Yuejin ^b  

^a VICTORIA UNIVERSITY OF WELLINGTON   (New Zealand)

^b AGRICULTURAL UNIVERSITY OF HEBEI   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; LINEAR EQUATIONS; MATHEMATICAL MODELS; PROBABILITY;

LINEAR LABEL REFINEMENT (LLR); NAIVE BAYES CLASSIFICATION; PROBABILISTIC LABEL REFINEMENT (PLR); TRAINING SET;

CLASSIFICATION (OF INFORMATION);

EID: 33749577904     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11801603_45     Document Type: Conference Paper

References (26)

1. Lewis, D.D.: Naive (bayes) at forty: The independence assumption in information retrieval. In Proceedings of the 10th European Conference on Machine Learning, Chemnitz, DE, Springer Verlag, Heidelberg, DE (1998) 4-15
2. Yang, Y.: Expert network: effective and efficient learning from human decisions in text categorisation and retrieval. In Proceedings of the 17th ACM International Conference on Research and Development in Information Retrieval, Dublin, IE, Springer Verlag (1994) 13-22
3. Lewis, D.D., Ringuette, M.: A comparison of two learning algorithms for text categorization. In: Proceedings of the 3rd Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, US (1994) 81-93
4. Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In Proceedings of the 10th European Conference on Machine Learning, Springer, (1998) 137-142
5. Nigam, K., Lafferty, J., McCallum, A.: Using Maximum Entropy for Text Classification. In: IJCAI-99 Workshop on Machine Learning for Information Filtering. (1999) 61-67
6. Ruiz, M.E., Srinivasan, P.: Hierarchical neural networks for text categorization. In Proceedings of the 22nd ACM International Conference on Research and Development in Information Retrieval, ACM Press (1999) 281-282
7. Brin, S., Page, L.: The anatomy of a Large-scale Hypertextual Web search Engine. Computer Networks and ISDN Systems 30(1-7) (1998) 107-117
8. Chakrabarti, S., Dom, B.E., Indyk, P.: Enhanced hypertext categorization using hyperlinks. In Proceedings of the ACM International Conference on Management of Data, ACM Press (1998) 307-318
9. Oh, H.J., Myaeng, S.H., Lee, M.H.: A practical hypertext categorization method using links and incrementally available class information. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval, ACM Press (2000) 264-271
10. Getoor, L., Segal, E., Taskar, B., Koller, D.: Probabilistic Models of Text and Link Structure for Hypertext Classification. In IJCAI Workshop on Text Learning: Beyond Supervision (2001)
11. Lu, Q., Getoor, L.: Link-based classification using labeled and unlabeled data. In: ICML Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining, Washington DC. (2003)
12. Lu, Q., Getoor, L.: Link-based text classification. In: IJCAI Workshop on Text Mining and Link Analysis. Acapulco, MX. (2003)
13. Lu, Q., Getoor, L.: Link-based classification. In: International Conference on Machine Learning, Washington, DC. (2003)
14. Taskar, B., Segal, E., Koller, D.: Probabilistic Classification and Clustering in Relational Data. In Proceeding of the 17th International Joint Conference on Artificial Intelligence (2001) 870-878
15. Craven, M., Slattery, S.: Relational Learning with Statistical Predicate Invention: Better Models for Hypertext. Mach. Learn. 43(1-2) (2001) 97-119
16. Quinlan, J.R.: Learning logical definitions from relations. Mach. Learn. 5(3) (1990) 239-266
17. Cao, M.D., Gao, X.: Combining contents and citations for scientific document classification. In: Proceedings of 18th Australian Joint Conference on Artificial Intelligence, Springer (2005) 143-152
18. Lewis, D.D.: Naive (Bayes) at forty: The independence assumption in information retrieval. In: Proceedings of the 10th European Conference on Machine Learning. Springer, (1998) 4-15
19. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Network of Plausible Inference. Morgan Kaufmann publishers (1988)
20. Ghahramani, Z.: Graphical Models: Parameter Learning. In Handbook of Brain Theory and Neural Networks. 2 edn. MIT Press (2003) 486-490
21. Jordan, M.I., Weiss, Y.: Graphical Models: Probabilistic Inference. In Handbook of Brain Theory and Neural Networks. 2 edn. MIT Press (2003) 490-496
22. Yedidia, J.S., Freeman, W.T., Weiss, Y.: Understanding Belief Propagation and its Generalizations. Technical Report TR-2001-22, Mitsubishi Electric Research Laboratories, Inc. (2002)
23. Cowell, R.: Introduction in Inference in Bayesian Networks. MIT Press, Cambridge, MA, USA (1999) 9-26
24. Russell, S., Novig, P.: Artificial Intelligence: A Modern Approach. 2 edn. Prentice Hall (2005)
25. MacKay, D.J.C.: Introduction to Monte Carlo Methods. In: Learning in graphical models. MIT Press, Cambridge, MA, USA (1999) 175-204
26. McCallum, A.K., Nigam, K., Rennie, J., Seymore, K.: Automating the Construction of Internet Portals with Machine Learning. Information Retrieval 3(2) (2000) 127-163