Combining structural and citation-based evidence for text classification

ABSTRACT

This paper discusses how citation-based information and structural content (e.g., title, abstract) can be combined to improve classification of text documents into predefined categories. We evaluate different measures of similarity derived from the citation structure and the structural content of the collection, and determine how they can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our empirical experiments using documents from the ACM Digital Library and the ACM Computing Classification System show that we can discover similarity functions that work better than using evidence in isolation and whose combined performance through a simple majority voting is comparable to that of Support Vector Machine classifiers.

REFERENCES

• 1: Norbert Gövert , Mounia Lalmas , Norbert Fuhr, A probabilistic description-oriented approach for categorizing web documents, Proceedings of the eighth international conference on Information and knowledge management, p.475-482, November 02-06, 1999, Kansas City, Missouri, United States [doi>10.1145/319950.320053]
• 2: John R. Koza, Genetic programming: on the programming of computers by means of natural selection, MIT Press, Cambridge, MA, 1992
• 3: Ashwin Srinivasan, A Study of Two Sampling Methods for Analyzing Large Datasets with ILP, Data Mining and Knowledge Discovery, v.3 n.1, p.95-123, March 1999 [doi>10.1023/A:1009824123462]

INDEX TERMS

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

Additional Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.4 Applications
          Subjects: Text processing

General Terms:
Algorithms, Experimentation, Measurement, Performance

Keywords:
GP, citation analysis, classification, document similarity