Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Dynamic reference sifting: a case study in the homepage domain
Selected papers from the sixth international conference on World Wide Web
Learning in graphical models
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Effective site finding using link anchor information
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
The Importance of Prior Probabilities for Entry Page Search
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Machine Learning Approach for Homepage Finding Task
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Query-independent evidence in home page finding
ACM Transactions on Information Systems (TOIS)
Combining document representations for known-item search
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Social Network Extraction of Academic Researchers
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Entity information management in complex networks
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Foundations and Trends in Information Retrieval
A joint classification method to integrate scientific and social networks
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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Faculty homepage discovery is an important step toward building an academic portal. Although the general homepage finding tasks have been well studied (e.g., TREC-2001 Web Track), faculty homepage discovery has its own special characteristics and not much focused research has been conducted for this task. In this paper, we view faculty homepage discovery as text categorization problems by utilizing Yahoo BOSS API to generate a small list of high-quality candidate homepages. Because the labels of these pages are not independent, standard text categorization methods such as logistic regression, which classify each page separately, are not well suited for this task. By defining homepage dependence graph, we propose a conditional undirected graphical model to make joint predictions by capturing the dependence of the decisions on all the candidate pages. Three cases of dependencies among faculty candidate homepages are considered for constructing the graphical model. Our model utilizes a discriminative approach so that any informative features can be used conveniently. Learning and inference can be done relatively efficiently for the joint prediction model because the homepage dependence graphs resulting from the three cases of dependencies are not densely connected. An extensive set of experiments have been conducted on two testbeds to show the effectiveness of the proposed discriminative graphical model.