Kernel-based reranking for named-entity extraction

Truc Vien T. Nguyen, Alessandro Moschitti, Giuseppe Riccardi

Research output: Contribution to conferencePaper


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.

Original languageEnglish
Number of pages9
Publication statusPublished - 1 Dec 2010
Event23rd International Conference on Computational Linguistics, Coling 2010 - Beijing, China
Duration: 23 Aug 201027 Aug 2010


Other23rd International Conference on Computational Linguistics, Coling 2010


ASJC Scopus subject areas

  • Language and Linguistics
  • Computational Theory and Mathematics
  • Linguistics and Language

Cite this

Nguyen, T. V. T., Moschitti, A., & Riccardi, G. (2010). Kernel-based reranking for named-entity extraction. 901-909. Paper presented at 23rd International Conference on Computational Linguistics, Coling 2010, Beijing, China.