Name Discrimination and Email Clustering using Unsupervised Clustering and Labeling of Similar Contexts

In this paper, we apply an unsupervised word sense discrimination technique based on clustering similar contexts (Purandare and Pedersen, 2004) to the problems of name discrimination and email clustering. Names of people, places, and organizations are not always unique. This can create a problem when we refer to or seek out information about such entities. When this occurs in written text, we show that we can cluster ambiguous names into unique groups by identifying which contexts are similar to each other. It has been previously shown by (Pedersen, Purandare, and Kulkarni, 2005) that this approach can be successfully used for discrimination of names with two-way ambiguity. Here we show that it can be extended to multiway distinctions as well. We adapt the cluster labeling technique introduced by (Kulkarni, 2005) for the multiway distinctions of name discrimination. On the similar lines of contextual similarity, we also observe that email messages can be treated as contexts, and that in clustering them together we are able to group them based on their underlying content rather than the occurrence of specific strings.