1998 EnhancedHypertextCategorizationUsingHyperlinks

(Chakrabarti et al., 1998c) ⇒ Soumen Chakrabarti, Byron Dom, and Piotr Indyk. (1998). “Enhanced Hypertext Categorization Using Hyperlinks.” In: Proceedings of the 1998 ACM SIGMOD Conference (SIGMOD 1998). doi:10.1145/276304.276332

Subject Headings: Local Collective Classification Algorithm.

Notes

Cited By

~696 http://scholar.google.com/scholar?q=%22Enhanced+Hypertext+Categorization+Using+Hyperlinks%22+1998

2005

(Macskassy & Provost, 2005) ⇒ Sofus Macskassy, and Foster Provost. (2005). “NetKit-SRL: A Toolkit for Network Learning and Inference and its use for classification of networked data.” In: ProceedingsAnn. Conference North Am. Assoc. Computational Social and Organizational Science (NAACSOS)
- For machine learning research on networked data, the watershed paper of Chakrabarti et al. (1998) studied classifying webpages based on the text and (possibly inferred) class labels of neighboring pages, using relaxation labeling paired with naive Bayes local and relational classifiers. In their experiments, using the link structure substantially improved classification over using the local (text) information alone. Further, considering the text of the neighbors generally hurt performance (based on the methods they used), whereas using only the (inferred) class labels improved performance. ...

Quotes

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!1 and the US Patent Database2. 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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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1998 EnhancedHypertextCategorizationUsingHyperlinks	Soumen Chakrabarti Byron Dom Piotr Indyk			Enhanced Hypertext Categorization Using Hyperlinks		Proceedings of the 1998 ACM SIGMOD Conference	http://www.cse.iitb.ac.in/~soumen/sigmod98.ps	10.1145/276304.276332		1998