Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Elements of information theory
Elements of information theory
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Readings in information retrieval
Readings in information retrieval
A New Term Significance Weighting Approach
Journal of Intelligent Information Systems
Effective measures for inter-document similarity
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In this paper, we present a method that can automatically evaluate performance of different term weighting schemes in information retrieval without resorting to precision-recall based on human relevance judgments. Specifically, the problem is: given two document-term matrixes generated from two different term weighting schemes, can we tell which term weighting scheme will performance better than the other? We propose a meta-scoring function, which takes as input the document-term matrix generated by some term weighting scheme and computes a goodness score from the document-term matrix. In our experiments, we found out that this score is highly correlated with the precision-recall measurement for all the collections and term weighting schema we tried. Thus, we conclude that our meta-scoring function can be a substitute for the precision-recall measurement that needs relevance judgments of human subject. Furthermore, this meta-scoring function is not limited only to text information retrieval can be applied to fields such as image and DNA retrieval.