Abstract
We present a probabilistic framework for inferring coreference relations among person names in a news collection. The approach does not assume any prior knowledge about persons (e.g. an ontology) mentioned in the collection and requires basic linguistic processing (named entity recognition) and resources (a dictionary of person names). The system parameters have been estimated on a 5K corpus of Italian news documents. Evaluation, over a sample of four days news documents, shows that the error rate of the system (1.4%) is above a baseline (5.4%) for the task. Finally, we discuss alternative approaches for evaluation.
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Popescu, O., Magnini, B. (2007). Inferring Coreferences Among Person Names in a Large Corpus of News Collections. In: Basili, R., Pazienza, M.T. (eds) AI*IA 2007: Artificial Intelligence and Human-Oriented Computing. AI*IA 2007. Lecture Notes in Computer Science(), vol 4733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74782-6_32
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DOI: https://doi.org/10.1007/978-3-540-74782-6_32
Publisher Name: Springer, Berlin, Heidelberg
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