Efficient Hierarchical Entity Classifier Using Conditional Random Fields

In this paper we develop an automatic classifier for a very large set of labels, the WordNet synsets. We employ Conditional Random Fields (CRFs) because of their flexibility to include a wide variety of nonindependent features. Training CRFs on a big number of labels proved a problem because of the large training cost. By taking into account the hypernym/hyponym relation between synsets in WordNet, we reduced the complexity of training from O(TM 2 NG) to O(T (logM) 2 NG) with only a limited loss in accuracy.

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