Conditional Random Fields: Discriminative Training over Statistical features for Named Entity Recognition
نویسندگان
چکیده
We describe the experiments of the two learning algorithms for Named Entity Recognition. One implements Conditional Random Fields (CRFs), another makes use of Support Vector Machines (SVMs). Both are trained with a large number of features. While SVMs employ purely input features, CRFs also exploit statistical aspects in terms of unigram and bigram of both features and output tags. The main characteristic of our approach is the use of different learning models for the task.
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