Research on Automatic Recognition of Auxiliary “DE”

doi:10.13209/j.0479-8023.2017.167

Acta Scientiarum Naturalium Universitatis Pekinensis ›› 2018, Vol. 54 ›› Issue (3): 466-474.DOI: 10.13209/j.0479-8023.2017.167

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Research on Automatic Recognition of Auxiliary “DE”

LIU Qiuhui¹, ZHANG Kunli^1,†, XU Hongfei¹, YU Shiwen², ZAN Hongying¹

1. School of Information Engineering, Zhengzhou University, Zhengzhou 450001
2. Key Laboratory of Computational Linguistics (MOE), Peking University, Beijing 100871

Received:2017-01-21 Revised:2017-11-26 Online:2018-05-20 Published:2018-05-20
Contact: ZHANG Kunli, E-mail: ieklzhang(at)zzu.edu.cn

助词“的”用法自动识别研究

刘秋慧¹, 张坤丽^1,†, 许鸿飞¹, 俞士汶², 昝红英¹

1. 郑州大学信息工程学院郑州 450001
2. 北京大学计算语言学教育部重点实验室, 北京 100871

通讯作者: 张坤丽, E-mail: ieklzhang(at)zzu.edu.cn
基金资助:
国家重点基础研究发展计划(2014CB340504)、国家自然科学基金(61402419, 60970083)、国家社会科学基金(14BYY096)、计算语言学教育部重点实验室开放课题项目、河南省科技厅基础研究项目(142300410231, 142300410308)、河南省教育厅科学技术研究重点项目(15A520098)和河南省科技厅科技攻关项目(172102210478)资助

Abstract

Abstract:

Based on the triune Chinese function word usage knowledge base (CFKB), the rule-based method, CRF (conditional random field) model and GRU (gated recurrent unit) are adopted to automatically recognize the usages of auxiliary “DE”, and the accuracy rates are 34.4%, 77.5% and 81.3% respectively. In order to improve the accuracy, some usages of auxiliary “DE” are combined and formed coarse-grained usage. The accuracy of CRF achieves 81.8%, and the accuracy of neural network model achieves 84.5%. It is expected that the recognition result of auxiliary “DE” can improve the performance of other NLP task.

Key words: “DE”, GRU (gated recurrent unit), rule, CRF (conditional random field)

摘要：

在“三位一体”虚词用法知识库的基础上, 分别采用基于规则、基于CRF模型和神经网络模型的门循环单元, 对助词“的”用法进行自动识别, 识别的准确率分别为 34.4%, 77.5%和81.3%。在对助词“的”用法进行分析的基础上, 合并了部分用法, 并采用CRF模型和神经网络模型进行粗粒度用法识别, 准确率分别达到81.8%和 84.5%, 得到较明显的提高。期望识别结果可以应用于其他自然语言处理任务中。

关键词: “的”, 门循环单元, 规则, CRF

CLC Number:

TP391

LIU Qiuhui, ZHANG Kunli, XU Hongfei, YU Shiwen, ZAN Hongying. Research on Automatic Recognition of Auxiliary “DE”[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2018, 54(3): 466-474.

刘秋慧, 张坤丽, 许鸿飞, 俞士汶, 昝红英. 助词“的”用法自动识别研究[J]. 北京大学学报（自然科学版）, 2018, 54(3): 466-474.

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https://xbna.pku.edu.cn/EN/Y2018/V54/I3/466

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Research on Automatic Recognition of Auxiliary “DE”

助词“的”用法自动识别研究

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