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A composite kernel for named entity recognition
2010
Pattern Recognition Letters
In this paper, we propose a novel kernel function for support vector machines (SVM) that can be used for sequential labeling tasks like named entity recognition (NER). Machine learning methods like support vector machines, maximum entropy, hidden Markov model and conditional random fields are the most widely used methods for implementing NER systems. The features used in machine learning algorithms for NER are mostly string based features. The proposed kernel is based on calculating a novel
doi:10.1016/j.patrec.2010.05.004
fatcat:lcct5thwkvgx5polrka4jfpvii