Knowledge-intensive word disambiguation via common-sense and wikipedia

Abstract

A promising approach to cope with the challenges that Word Sense Disambiguation brings is to use knowledge-intensive methods. Typically they rely on Wikipedia for supporting automatic concept identification. The exclusive use of Wikipedia as a knowledge base for word disambiguation and therefore the general identification of topics, however, have low accuracy vis-àvis texts with diverse topics, as can be the case with blogs. This motivated us to propose a method for word disambiguation that, in addition to the use of Wikipedia, uses a common sense database. Use of this base enriches the definition of the concepts previously identified with the help of Wikipedia, and permits the definition of a similarity measure between concepts, which is characterized by verifying the similarity of two concepts from the viewpoint of conceptual proximity in the Wikipedia hierarchy, in addition to the proximity between such concepts in terms of the inferences that they can make. We show that by doing this, we improved the accuracy of automatic disambiguation of words compared with methods that do not use a common sense base.