mining semantic distance between corpus terms
In this paper, we face two problems in classical semantic similarity measures. Firstly, the context-dependency problem in knowledge-base measures since no one takes into account the context of the target domain. That is, a multisource context-dependent approach is presented. Secondly, the coverage problem with these measures since similarities can only be calculated between concepts included in a taxonomy. Moreover, "pure" corpus-based measures are still way from achieving performance reached by knowledge based measures. We present a more complex corpus-based approach using a taxonomy and data mining techniques in order to compute semantic distances between terms uncovered by the taxonomy. Experiments made show clearly the effectiveness of both proposed approaches.
semantic similarity data mining knowledge acquisition context dependency