Multilingual news clustering: Feature translation vs. identification of cognate named entities

In this paper we evaluate the influence of different document representations in the results of multilingual news clustering. We aim at proving whether or not the use of only named entities is a good source of knowledge for multilingual news clustering. We compare two approaches: one based on feature translation, and another based on cognate identification. Our main contribution is using only some categories of cognate named entities like document representation features to perform multilingual news clustering, without the need of translation resources. The results show that the use of cognate named entities, as the only type of features to represent news, leads to good multilingual clustering performance, comparable to the one obtained by using the feature translation approach.

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