URL-based Web Page Classification - A New Method for URL-based Web Page Classification Using n-Gram Language Models

Tarek Amr Abdallah, Beatriz De La Iglesia

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

This paper is concerned with the classification of web pages using their Uniform Resource Locators (URLs) only. There is a number of contexts these days in which it is important to have an efficient and reliable classification of a web-page from the URL, without the need to visit the page itself. For example, emails or messages sent in social media may contain URLs and require automatic classification. The URL is very concise, and may be composed of concatenated words so classification with only this information is a very challenging task. Much of the current research on URL-based classification has achieved reasonable accuracy, but the current methods do not scale very well with large datasets. In this paper, we propose a new solution based on the use of an n-gram language model. Our solution shows good classification performance and is scalable to larger datasets. It also allows us to tackle the problem of classifying new URLs with unseen sub-sequences.
Original languageEnglish
Pages14
Number of pages21
DOIs
Publication statusPublished - Nov 2014
EventSCITEPRESS Digital Library - KDIR 2014 - International Conference on Knowledge Discovery and Information Retrieval - Italy, Rome, Italy
Duration: 13 Nov 2014 → …

Conference

ConferenceSCITEPRESS Digital Library - KDIR 2014 - International Conference on Knowledge Discovery and Information Retrieval
CountryItaly
CityRome
Period13/11/14 → …

Keywords

  • Language Models
  • Information Retrieval
  • Web Classification
  • Web Mining
  • Machine Learning

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