Abstract
The natural way to model a news corpus is as a directed graph where stories are linked to one another through a variety of relationships. We formalize this notion by viewing each news story as a set of actors, and by viewing links between stories as transformations these actors go through. We propose and model a simple and comprehensive set of transformations: create, merge, split, continue, and cease. These transformations capture evolution of a single actor and interactions among multiple actors. We present algorithms to rank each transformation and show how ranking helps us to infer important relationships between actors and stories in a corpus. We demonstrate the effectiveness of our notions by experimenting on large news corpora.
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© 2008 Springer-Verlag Berlin Heidelberg
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Choudhary, R., Mehta, S., Bagchi, A., Balakrishnan, R. (2008). Towards Characterization of Actor Evolution and Interactions in News Corpora. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds) Advances in Information Retrieval. ECIR 2008. Lecture Notes in Computer Science, vol 4956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78646-7_39
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DOI: https://doi.org/10.1007/978-3-540-78646-7_39
Publisher Name: Springer, Berlin, Heidelberg
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