NDLI: A probabilistic description-oriented approach for categorizing web documents

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A probabilistic description-oriented approach for categorizing web documents

Content Provider	ACM Digital Library
Author	Lalmas, Mounia Gövert, Norbert Fuhr, Norbert
Abstract	The automatic categorisation of web documents is becoming crucial for organising the huge amount of information available in the Internet. We are facing a new challenge due to the fact that web documents have a rich structure and are highly heterogeneous. Two ways to respond to this challenge are (1) using a representation of the content of web documents that captures these two characteristics and (2) using more effective classifiers.Our categorisation approach is based on a probabilistic description-oriented representation of web documents, and a probabilistic interpretation of the k-nearest neighbour classifier. With the former, we provide an enhanced document representation that incorporates the structural and heterogeneous nature of web documents. With the latter, we provide a theoretical sound justification for the various parameters of the k-nearest neighbour classifier.Experimental results show that (1) using an enhanced representation of web documents is crucial for an effective categorisation of web documents, and (2) a theoretical interpretation of the k-nearest neighbour classifier gives us improvement over the standard k-nearest neighbour classifier.
Starting Page	475
Ending Page	482
Page Count	8
File Format	PDF
ISBN	1581131461
DOI	10.1145/319950.320053
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1999-11-01
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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