Kernel-based Reranking for Named-Entity Extraction

We present novel kernels based on structured and unstructured features for reranking the N-best hypotheses of conditional random fields (CRFs) applied to entity extraction. The former features are generated by a polynomial kernel encoding entity features whereas tree kernels are used to model dependencies amongst tagged candidate examples. The experiments on two standard corpora in two languages, i.e. the Italian EVALITA 2009 and the English CoNLL 2003 datasets, show a large improvement on CRFs in F-measure, i.e. from 80.34% to 84.33% and from 84.86% to 88.16%, respectively. Our analysis reveals that both kernels provide a comparable improvement over the CRFs baseline. Additionally, their combination improves CRFs much more than the sum of the individual contributions, suggesting an interesting kernel synergy.

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Truc-Vien T. Nguyen, Alessandro Moschitti, Giuseppe Riccardi
• Keywords: COLING 2010, Computational Linguistics, Conditional Random Fields, CoNLL 2003 Datasets, Interesting Kernel Synergy