Liu, JingchenHuang, MinlieZhu, Xiaoyan2012-05-222012-05-2220102010-07Liu, J., Huang, M., & Zhu, X. (2010). Recognizing Biomedical Named Entities using Skip-chain Conditional Random Fields. Proceedings of the 2010 Workshop on Biomedical Natural Language Processing, Uppsala, SWE. (p. 10-18). Association for Computational Linguistics.http://hdl.handle.net/10625/49054Proceedings of the 2010 Workshop on Biomedical Natural Language ProcessingLinear-chain Conditional Random Fields (CRF) has been applied to perform the Named Entity Recognition (NER) task in many biomedical text mining and information extraction systems. However, the linear-chain CRF cannot capture long distance dependency, which is very common in the biomedical literature. In this paper, we propose a novel study of capturing such long distance dependency by defining two principles of constructing skip-edges for a skip-chain CRF: linking similar words and linking words having typed dependencies. The approach is applied to recognize gene/protein mentions in the literature. When tested on the BioCreAtIvE II Gene Mention dataset and GENIA corpus, the approach contributes significant improvements over the linear-chain CRF. We also present in-depth error analysis on inconsistent labeling and study the influence of the quality of skip edges on the labeling performance.Text1 digital file (p. 10-18 : ill.)enRANDOM FIELDINFORMATION RETRIEVALBIOMEDICAL DATAERROR ANALYSISLABELLINGConference Paper