Abstract
This thesis presents novel measures to estimate the degree of semantic similarity between words using one or more knowledge sources. Several evaluations show that they improve the accuracy of related works. These measures have been applied to clustering to compute the similarity/distance between individuals described by textual attributes. Clustering results show that a proper interpretation of textual data at a semantic level improves the quality of the clusters and ease their interpretation.