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Enhanced Hypertext Categorization Using Hyperlinks.

Soumen Chakrabarti, Byron Dom, Piotr Indyk: Enhanced Hypertext Categorization Using Hyperlinks. SIGMOD Conference 1998: 307-318
@inproceedings{DBLP:conf/sigmod/ChakrabartiDI98,
  author    = {Soumen Chakrabarti and
               Byron Dom and
               Piotr Indyk},
  editor    = {Laura M. Haas and
               Ashutosh Tiwary},
  title     = {Enhanced Hypertext Categorization Using Hyperlinks},
  booktitle = {SIGMOD 1998, Proceedings ACM SIGMOD International Conference
               on Management of Data, June 2-4, 1998, Seattle, Washington, USA},
  publisher = {ACM Press},
  year      = {1998},
  isbn      = {0-89791-995-5},
  pages     = {307-318},
  ee        = {http://doi.acm.org/10.1145/276304.276332, db/conf/sigmod/ChakrabartiDI98.html},
  crossref  = {DBLP:conf/sigmod/98},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

A major challenge in indexing unstructured hypertext databases is to automatically extract meta-data that enables structured search using topic taxonomies, circumvents keyword ambiguity, and improves the quality of search and profile-based routing and filtering. Therefore, an accurate classifier is an essential component of a hypertext database. Hyperlinks pose new problems not addressed in the extensive text classification literature. Links clearly contain high-quality semantic clues that are lost upon a purely term-based classifier, but exploiting link information is non-trivial because it is noisy. Naive use of terms in the link neighborhood of a document can even degrade accuracy. Our contribution is to propose robust statistical models and a relaxation labeling technique for better classification by exploiting link information in a small neighborhood around documents. Our technique also adapts gracefully to the fraction of neighboring documents having known topics. We experimented with pre-classified samples from Yahoo! and the US Patent Database. In previous work, we developed a text classifier that misclassified only 13% of the documents in the well-known Reuters benchmark; this was comparable to the best results ever obtained. This classifier misclassified 36% of the patents, indicating that classifying hypertext can be more difficult than classifying text. Naively using terms in neighboring documents increased error to 38%; our hypertext classifier reduced it to 21%. Results with the Yahoo! sample were more dramatic: the text classifier showed 68% error, whereas our hypertext classifier reduced this to only 21%.

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Printed Edition

Laura M. Haas, Ashutosh Tiwary (Eds.): SIGMOD 1998, Proceedings ACM SIGMOD International Conference on Management of Data, June 2-4, 1998, Seattle, Washington, USA. ACM Press 1998, ISBN 0-89791-995-5 BibTeX , SIGMOD Record 27(2), June 1998
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[Abstract]
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References

[1]
...
[2]
Chidanand Apté, Fred Damerau, Sholom M. Weiss: Automated Learning of Decision Rules for Text Categorization. ACM Trans. Inf. Syst. 12(3): 233-251(1994) BibTeX
[3]
...
[4]
Soumen Chakrabarti, Byron Dom, Rakesh Agrawal, Prabhakar Raghavan: Using Taxonomy, Discriminants, and Signatures for Navigating in Text Databases. VLDB 1997: 446-455 BibTeX
[5]
...
[6]
...
[7]
W. Bruce Croft, Howard R. Turtle: A Retrieval Model for Incorporating Hypertext Links. Hypertext 1989: 213-224 BibTeX
[8]
...
[9]
...
[10]
...
[11]
David Eppstein: Finding the k Shortest Paths. FOCS 1994: 154-165 BibTeX
[12]
Daniela Florescu, Daphne Koller, Alon Y. Levy: Using Probabilistic Information in Data Integration. VLDB 1997: 216-225 BibTeX
[13]
Hans-Peter Frei, D. Stieger: Making Use of Hypertext Links when Retrieving Information. ECHT 1992: 102-111 BibTeX
[14]
Hans-Peter Frei, D. Stieger: The Use of Semantic Links in Hypertext Information Retrieval. Inf. Process. Manage. 31(1): 1-13(1995) BibTeX
[15]
...
[16]
Marti A. Hearst, Chandu Karadi: Cat-a-Cone: An Interactive Interface for Specifying Searches and Viewing Retrieval Results using a Large Category Hierarchy. SIGIR 1997: 246-255 BibTeX
[17]
...
[18]
...
[19]
...
[20]
...
[21]
...
[22]
...
[23]
...
[24]
...
[25]
Manish Mehta, Rakesh Agrawal, Jorma Rissanen: SLIQ: A Fast Scalable Classifier for Data Mining. EDBT 1996: 18-32 BibTeX
[26]
...
[27]
...
[28]
...
[29]
...
[30]
...
[31]
Gerard Salton: Associative Document Retrieval Techniques Using Bibliographic Information. J. ACM 10(4): 440-457(1963) BibTeX
[32]
...
[33]
...
[34]
...
[35]
...
[36]
...
[37]
John C. Shafer, Rakesh Agrawal, Manish Mehta: SPRINT: A Scalable Parallel Classifier for Data Mining. VLDB 1996: 544-555 BibTeX
[38]
William W. Cohen, Yoram Singer: Context-sensitive Learning Methods for Text Categorization. SIGIR 1996: 307-315 BibTeX
[39]
John R. Smith, Shih-Fu Chang: Visually Searching the Web for Content. IEEE MultiMedia 4(3): 12-20(1997) BibTeX
[40]
...

Referenced by

  1. Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, D. Sivakumar, Andrew Tomkins, Eli Upfal: The Web as a Graph. PODS 2000: 1-10
  2. Minos N. Garofalakis, Rajeev Rastogi, S. Seshadri, Kyuseok Shim: Data Mining and the Web: Past, Present and Future. Workshop on Web Information and Data Management 1999: 43-47
  3. Ke Wang, Senqiang Zhou, Shiang Chen Liew: Building Hierarchical Classifiers Using Class Proximity. VLDB 1999: 363-374
  4. Soumen Chakrabarti, Martin van den Berg, Byron Dom: Distributed Hypertext Resource Discovery Through Examples. VLDB 1999: 375-386
  5. Soumen Chakrabarti, Byron Dom, Rakesh Agrawal, Prabhakar Raghavan: Scalable Feature Selection, Classification and Signature Generation for Organizing Large Text Databases into Hierarchical Topic Taxonomies. VLDB J. 7(3): 163-178(1998)
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