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In the last decade, many diverse advances have occurred in the field ofinformation extraction from data. Information extraction in its simplest formtakes place in computing environments, where structured data can be extractedthrough a series of queries. The continuous expansion of quantities of datahave therefore provided an opportunity for knowledge extraction (KE) from atextual document (TD). A typical problem of this kind is the extraction ofcommon characteristics and knowledge from a group of TDs, with the possibilityto group such similar TDs in a process known as clustering. In this paper wepresent a technique for such KE among a group of TDs related to the commoncharacteristics and meaning of their content. Our technique is based on theSpearman's Rank Correlation Coefficient (SRCC), for which the conductedexperiments have proven to be comprehensive measure to achieve a high-qualityKE.

A Measure of Similarity in Textual Data Using Spearman's Rank Correlation Coefficient