Entity-based cross-document coreferencing using the Vector Space Model
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Dynamic parameters for cross document coreferece
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Methods of estimating the number of clusters for person cross document coreference task
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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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.