  Clustering web documents has numerous applications, such as aggregating news
articles into meaningful events, detecting trends and hot topics on the Web,
preserving diversity in search results, etc. At the same time, the importance
of named entities and, in particular, the ability to recognize them and to
solve the associated co-reference resolution problem are widely recognized as
key enabling factors when mining, aggregating and comparing content on the Web.
  Instead of considering these two problems separately, we propose in this
paper a method that tackles jointly the problem of clustering news articles
into events and cross-document co-reference resolution of named entities. The
co-occurrence of named entities in the same clusters is used as an additional
signal to decide whether two referents should be merged into one entity. These
refined entities can in turn be used as enhanced features to re-cluster the
documents and then be refined again, entering into a virtuous cycle that
improves simultaneously the performances of both tasks. We implemented a
prototype system and report results using the TDT5 collection of news articles,
demonstrating the potential of our approach.
