doi: 10.4304/jsw.6.3.413-420
An Improvement to TF-IDF: Term Distribution based Term Weight Algorithm
- 1 Shanghai Second Polytechnic University, Shanghai, China
- 2 Central University of Finance and Economics, Beijing, China
Abstract
In the process of document formalization, term weight algorithm plays an important role. It greatly interferes the precision and recall results of the natural language processing (NLP) systems. Currently, TF-IDF term weight algorithm is widely applied into language models to build NLP Systems. Since term frequency is not the only discriminator which is necessary to be considered in term weighting and make each weight suitable to indicate the term’s importance, we are motivated to investigate other statistical characteristics of terms and found an important discriminator: term distribution. Furthermore, we found that, in a single document, a term with higher frequency and close to hypodispersion distribution usually contains much semantic information and should be given higher weight. One the other hand, in a document collection, the term with higher frequency and hypo-dispersion distribution usually contains less information. Based on this hypothesis, by leveraging the Pearson Chisquare Test Statistic, a Term Distribution based Local Term Weight Algorithm and Global Term Weight Algorithm are put forward respectively in this paper. Also, the experiment results at the end of this paper approve the reliability and efficiency of the algorithms.
Keywords
- TF
- IDF
- Term Weight
- Natural Language P
How to Cite
Tian Xia and Yanmei Chai, "An Improvement to TF-IDF: Term Distribution based Term Weight Algorithm," Journal of Software, vol. 6, no. 3, pp. 413-420, 2011. https://doi.org/ 10.4304/jsw.6.3.413-420
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).