Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2014.050826
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 5 Issue 8, 2014.
Abstract: It seems that the term dependence methods developed using the expected mutual information measure (EMIM) have not achieved their potential in many areas of science, involving statistical text analysis or document processing. This study examines the reasons for the failure and highlights potential problems of applications. Several interesting questions are arisen, including, does a term provide any information if it occurs in all the sample documents? how the mutual information of two terms, under their status values, makes contribution to EMIM? are two terms highly dependent for their co-occurrence if they receive a high positive EMIM value? what may imply for dependence of two term pairs when they receive the same EMIM value? how can properly verify two terms to be high dependent for their cooccurrence? how can properly apply EMIM? does the size of the sample set matter? This study attempts to answer these questions in order to clarify confusions caused by the problems and/or suggest solutions to the problems. Some interesting examples are provided to clarify our viewpoints.
D. Cai, “Reconsideration of Potential Problems of Applying EMIM for Text Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 5(8), 2014. http://dx.doi.org/10.14569/IJACSA.2014.050826