Recognizing biomedical named entities using skip-chain conditional random fields

Authors:
Jingchen Liu;Minlie Huang;Xiaoyan Zhu
Affiliations:
Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China
Venue:
BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
Year:
2010

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Abstract

Linear-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.