An approach to automatic classification of text for information retrieval

ABSTRACT

In this paper, we explore an approach to make better use of semi-structured documents in information retrieval in the domain of biology. Using machine learning techniques, we make those inherent structures explicit by XML markups. This marking up has great potentials in improving task performance in specimen identification and the usability of online flora and fauna.

REFERENCES

• 1: Flora of North America (http://hua.huh.harvard.edu/FNA/). {Accessed 11 February 2002}
• 2: McCallum, A. K. Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering.http://www.cs.cmu.edu/~mccallum/bow 1996
• 3: Christopher J. C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, v.2 n.2, p.121-167, June 1998 [doi>10.1023/A:1009715923555]
• 4: Thorsten Joachims, Text Categorization with Suport Vector Machines: Learning with Many Relevant Features, Proceedings of the 10th European Conference on Machine Learning, p.137-142, April 21-23, 1998
• 5: Stephen Soderland, Learning Information Extraction Rules for Semi-Structured and Free Text, Machine Learning, v.34 n.1-3, p.233-272, Feb. 1999 [doi>10.1023/A:1007562322031]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.4 Systems and Software

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Concept learning

General Terms:
Design, Experimentation, Performance

Keywords:
Flora of North America, XML, automatic classification, machine learning