A novel TF-IDF weighting scheme for effective ranking
Jiaul H. Paik
Abstract
Jiaul H. Paik
Abstract
Term weighting schemes are central to the study of information retrieval systems. This article proposes a novel TF-IDF term weighting scheme that employs two different within document term frequency normalizations to capture two different aspects of term saliency. One component of the term frequency is effective for short queries, while the other performs better on long queries. The final weight is then measured by taking a weighted combination of these components, which is determined on the basis of the length of the corresponding query.
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Term weighting schemes are central to the study of information retrieval systems. This article proposes a novel TF-IDF term weighting scheme that employs two different within document term frequency normalizations to capture two different aspects of term saliency. One component of the term frequency is effective for short queries, while the other performs better on long queries. The final weight is then measured by taking a weighted combination of these components, which is determined on the basis of the length of the corresponding query.
Key concepts: Weighting, Ranking (information retrieval), Term (time), Computer science, tf–idf, Scheme (mathematics), Data mining, Basis (linear algebra)