A Bio-Entity Recognition Algorithm for Literature by Conditional Random Field Model Based on Improved Particle Swarm Optimizer

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A Bio-Entity Recognition Algorithm for Literature by Conditional Random Field Model Based on Improved Particle Swarm Optimizer

更新时间：2025-07-09

- A Bio-Entity Recognition Algorithm for Literature by Conditional Random Field Model Based on Improved Particle Swarm Optimizer
- Vol. 44, Issue 12, Pages: 38-42+124(2010)
- 作者机构：
  
  西安电子科技大学计算机学院,西安,710071
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP301
- Online First：10 December 2010，
  
  Published：2010
- 稿件说明：
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A Bio-Entity Recognition Algorithm for Literature by Conditional Random Field Model Based on Improved Particle Swarm Optimizer[J]. 2010, 44(12): 38-42+124.
DOI：

A Bio-Entity Recognition Algorithm for Literature by Conditional Random Field Model Based on Improved Particle Swarm Optimizer[J]. 2010, 44(12): 38-42+124. DOI：

摘要

针对生物医学文本中传统生物实体识别算法的精确度不高的问题

提出了一种新的基于粒子群优化-条件随机域的生物实体识别算法.新算法利用改进的粒子群优化算法训练条件随机域模型

并将训练后的条件随机域模型应用到生物实体的识别上.改进的粒子群优化算法引入粒子群聚集度来防止粒子群过早地陷入局部收敛

用迭代间对数似然相对变化率来控制算法的收敛

用线性变化的惯性因子和学习因子来控制搜索范围.实验结果表明

基于改进粒子群优化的条件随机域模型较隐马尔科夫模型、最大熵马尔科夫模型、支持向量机以及传统条件随机域模型等方法具有更高的精确率和召回率.

Abstract

A new bio-entity recognition algorithm is proposed to improve the precision of bio-entity recognition for biomedical literature. The new algorithm trains the conditional random field model using an improved particle swarm optimizer

and then applies the trained conditional random field model to bio-entity recognition. The aggregation degree of particle swarm is utilized to control the early local convergence of the particle swarm optimizer

the relative change ratio of log-likelihood between iterations is employed to end its iterations

and the inertia factor and learning factor are set as linear variables to control the scope of search space. Experimental results show that the proposed algorithm outperforms the models of HMM

MEMM

SVM and traditional L-BFGS CRF on precision and recall.

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references

LAFFERTY J, MCCALLUM A, PEREIRA F. Conditional random fields: probabilistic models for segmenting and labeling sequence data[C]∥Proceedings of the 18th International Conference on Machine Learning. Williamstown, Massachusetts, USA: Morgan Kaufmann, 2001:282-289.

KINJO AR, ROSSELLO F, VALIENTE G. Profile conditional random fields for modeling protein families with structural information [J]. Biophysics, 2009,5: 37-44.

SETTLES B. Biomedical named entity recognition using conditional random fields and novel feature sets[C]∥Proceedings of the Joint Workshop on Natural Language Processing in Biomedicine and Its Applications. New Jersey, USA: Association for Computational Linguistics, 2004:104-107.

BUNDSCHUS M, DEJORI M, STETTER M, et al. Extraction of semantic biomedical relations from text using conditional random fields [J]. BMC Bioinformatics, 2008,9: 207-220.

李国臣, 王瑞波, 李济洪. 基于条件随机场模型的汉语功能块自动标注[J]. 计算机研究与发展, 2010,47(2):336-343.

LI Guochen, WANG Ruibo, LI Jihong. Automatic labeling of Chinese functional chunks based on conditional random fields model [J]. Journal of Computer Research and Development, 2010,47(2):336-343.

KIM J D, OHTA T, TATEISI Y, et al.GENIA corpus: a semantically annotated corpus for bio-textmining [J]. Bioinformatics, 2003,19(S1): i180-i182.

TANABE L, XIE N, THOM L H, et al. Genetag: a tagged corpus for gene/protein named entity recognition [J]. BMC bioinformatics, 2005,6(S1): 1-7.

KENNEDY J, EBERHART R. Particle swarm optimization[C]∥Proceedings of the 14th International Conference on Neural Networks. Piscataway, NJ, USA: IEEE Service Center,1995: 1942-1948.

EBERHART R, KENNEDY J. A new optimizer using particle swarm theory [C]∥Proceedings of the 6th International Symposium on Micro Machine and Human Science. Piscataway, NJ, USA: IEEE, 1995:39-43.

YANG Guangyou. A modified particle swarm optimizer algorithm [C]∥Proceedings of the 8th International Conference on Electronic Measurement and Instruments. Piscataway, NJ, USA: IEEE, 2007:2675-2679.

MALOUF R. A comparison of algorithms for maximum entropy parameter estimation[C]∥Proceedings of The 6th Conference on Natural Language Learning. New Jersey, USA: Association for Computational Linguistics, 2002: 49-55.

崔舒宁, 朱丹军, 冯博琴, 等. 结合受控词汇表的生物基因本体标注与分类 [J]. 西安交通大学学报, 2008,42(2): 171-174.

CUI Shuning, ZHU Danjun, FENG Boqin, et al. Triage and annotation of biological gene's ontology combined controlled glossary [J]. Journal of Xi'an Jiaotong University, 2008, 42(2): 171-174.

吕强,刘士荣. 一种信息充分交流的粒子群优化算法 [J]. 电子学报,2010,38(3):664-667.

LV Qiang, LIU Shirong. A particle swarm optimization algorithm with fully communicated information [J]. Acta Electronica Sinica, 2010, 38(3): 664-667.

朱海, 王宇平, 权义宁,等.采用离散粒子群算法的网格任务安全级调度 [J].西安交通大学学报, 2010,21(6): 21-26.

ZHU Hai, WANG Yuping, QUAN Yining, et al. Security scheduling of grid tasks based on the discrete particle swarm algorithm [J]. Journal of Xi'an Jiaotong University, 2010,21(6):21-26.

王峰,邢科义,徐小平. 系统辨识的粒子群优化方法[J]. 西安交通大学学报,2009,43(2):116-120.

WANG Feng, XING Keyi, XU Xiaoping. A system identification method using particle swarm optimization [J]. Journal of Xi'an Jiaotong University, 2009,43(2): 116-120.

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