Generating better concept hierarchies using automatic document classification

ABSTRACT

This paper presents a hybrid concept hierarchy development technique for web returned documents retrieved by a meta-search engine. The aim of the technique is to separate the initial retrieved documents into topical oriented categories, prior to the actual concept hierarchy generation. The topical categories correspond to different semantic aspects of the query. This is done using a 1-of-n automatic document classification, on the initial set of returned documents. Then, an individual topical concept hierarchy is automatically generated inside each of the resulted categories. Both steps are executed on the fly at retrieval time. Due to the efficiency constraints imposed by the web retrieval context, the algorithm only uses document snippets (rather than full web pages) for both document classification and concept hierarchy generation. Experimental results show that the algorithm is able to improve the quality of the concept hierarchy presented to the searcher; at the same time, the efficiency parameters are kept within reasonable intervals.

REFERENCES

• 1: Eric Brill, Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging, Computational Linguistics, v.21 n.4, p.543-565, December 1995
• 2: Mark Sanderson , Bruce Croft, Deriving concept hierarchies from text, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, p.206-213, August 15-19, 1999, Berkeley, California, United States [doi>10.1145/312624.312679]
• 3: Wu, Y. B., C. Rakthin and C. Li (2002). "Summarizing Search Results with Automatic Table of Contents." AMCIS 2002, Dallas, TX: pp 88--92.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Clustering

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

General Terms:
Algorithms

Keywords:
automatic classification, concept hierarchy, document classification, information retrieval, manual classification