New Techniques for Relevant Word Ranking and Extraction

Abstract

In this paper we first propose two new metrics to rank the relevance of words in a text. The metrics presented are purely statistic and language independent and are based in the analysis of each word’s neighborhood. Typically, a relevant word is more strongly connected to some of its neighbors in despite of others. We also present a new technique based on the syllable analysis and show that despite it can be a metric by itself, it can also improve the quality of the proposed methods as also greatly improve the quality of other proposed methods (such as Tf-idf). Finally, based on the rankings previously obtained and using another neighborhood analysis, we present a new method to decide about the relevance of words on a yes/no basis.