基于深层条件随机场的生物医学命名实体识别

摘要

生物医学命名实体识别是从生物医学文献中获取关键知识的基础与关键任务.文中提出基于深层条件随机场的生物医学命名实体识别方法,构建多层结构的深层条件随机场模型,在不同层次的特征上结合增量式学习策略,选择最优特征集.最后通过基于〈全名,缩写〉对和基于领域信息的错误纠正算法,进一步修正识别结果.在生物医学命名实体评测语料JNLPBA上的实验验证文中方法的有效性.%Biomedical named entity recognition is the fundamental and key step in bioinformatics. In this paper, a biomedical named entity recognition method based on deep conditional random fields is proposed. The deep conditional random fields of multi-layer structure are constructed by stacking the linear-chain conditional random fields and the optimal feature set is built by incremental learning strategy. Finally, error correction algorithm based on full name-abbreviation and error correction algorithm based on domain knowledge are adopted for further modifying the recognition results. Experiments are conducted on the biomedical named entity recognition corpus JNLPBA, and the resultsdemonstrate the effectiveness of the proposed method.