计算机科学 ›› 2015, Vol. 42 ›› Issue (7): 15-18.doi: 10.11896/j.issn.1002-137X.2015.07.004

• 目次 • 上一篇    下一篇

面向军事文本的命名实体识别

冯蕴天 张宏军 郝文宁   

  1. 解放军理工大学指挥信息系统学院 南京210007
  • 出版日期:2018-11-14 发布日期:2018-11-14

Named Entity Recognition for Military Text

FENG Yun-tian ZHANG Hong-jun HAO Wen-ning   

  • Online:2018-11-14 Published:2018-11-14

摘要: 针对军事文本中的命名实体,提出一种基于条件随机场模型的半监督命名实体识别方法,旨在将人员军职军衔名、军事装备名、军用物资名、军事设施名、军事机构名(含部队番号)以及军用地名等军事命名实体的识别融合到一个统一的技术框架中。该方法针对军事文本的语法特点建立高效的特征集合,建立条件随机场模型对军事命名实体进行识别,并依次使用基于词典的方法和基于规则的方法对识别结果进行校正。实验表明,该方法在军事文本中能够出色地完成命名实体识别任务,在测试语料上的F-值最高达到90.9%,接近通用领域中命名实体识别的水平。

关键词: 军事文本,命名实体识别,条件随机场,半监督学习,军事信息处理

Abstract: This paper presented a semi-supervised named entity recognition method based on conditional random field model for named entities in the military text,which aims at merging military named entities such as military appointment and military rank,military equipment,military supplies,military facilities,military institutions (including code designation) and military place names into a unified technical framework.The method establishes an efficient feature set according to the grammatical features of the military text,builds conditional random field model to identify the military named entities,and develops the method based on dictionary and the method based on rules to improve the results in turn.Experiments show that the method is able to complete the named entity recognition task in the military text well,and make F-value about testing the language material up to 90.9%,which is close to the level of named entity recognition in the commons area.

Key words: Military text,Named entity recognition,Conditional random field,Semi-supervised learning,Military information processing

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