基于深层条件随机场的生物医学命名实体识别<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201611005

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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.

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	孙晓
	孙重远
	任福继

关键词 ：生物医学命名实体识别, 深层条件随机场, 增量式学习, 错误纠正算法

Abstract：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 results demonstrate the effectiveness of the proposed method.

Key words： Biomedical Named Entity Recognition Deep Condition Random Fields Incremental Learning Strategy Error Correction Algorithm

收稿日期: 2016-01-15

基金资助:国家自然科学基金重点项目(No.61432004)、安徽省自然科学基金项目(No.1508085QF119)、中国博士后科学基金项目(No.2015M580532)、模式识别国家重点实验室开放课题(No.201407345)资助

作者简介: 孙晓(通讯作者),男,1980年生,博士,副教授,主要研究方
向为自然语言处理、机器学习.E-mail:sunx@hfut.edu.cn.
孙重远,男,1992年生,硕士研究生,主要研究方向为自然语言处理、模式识别.E-mail:sunchongyuan@mail.hfut.edu.cn.
任福继,男,1959年生,博士,教授,主要研究方向为人工智能、情感计算.E-mail:ren@is.tokushima.ac.jp.

引用本文:

孙晓，孙重远，任福继. 基于深层条件随机场的生物医学命名实体识别^*[J]. 模式识别与人工智能, 2016, 29(11): 997-1008. SUN Xiao, SUN Chongyuan, REN Fuji. Biomedical Named Entity Recognition Based on Deep Conditional Random Fields. , 2016, 29(11): 997-1008.

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http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201611005 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2016/V29/I11/997

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