基于深度学习的中文地名识别研究
发布时间：2018-11-03 08:08
【摘要】：基于深度学习的循环神经网络方法,面向中文字和词的特点,重新定义了地名标注的输入和输出,提出了汉字级别的循环网络标注模型.以词级别的循环神经网络方法为基准,本文提出的字级别模型在中文地名识别的准确率、召回率和F值均有明显提高,其中F值提高了2.88%.在包含罕见词时提高更为明显,F值提高了26.41%.
[Abstract]:Based on the circular neural network method of deep learning and the characteristics of Chinese characters and words, the input and output of place names are redefined, and a Chinese character level circular network annotation model is proposed. Based on the word-level cyclic neural network method, the accuracy, recall rate and F value of the word-level model in Chinese geographical names recognition are obviously improved, in which the F value is increased by 2.88. The value of F was increased by 26.41 when the rare words were included.
【作者单位】： 南京理工大学经济管理学院;计算机软件新技术国家重点实验室(南京大学);
【基金】：国家自然科学基金资助项目(71503124,71303120) 江苏省社会科学基金资助项目(15TQC03)
【分类号】：TP18;TP391.1
，
本文编号：2307210
[Abstract]:Based on the circular neural network method of deep learning and the characteristics of Chinese characters and words, the input and output of place names are redefined, and a Chinese character level circular network annotation model is proposed. Based on the word-level cyclic neural network method, the accuracy, recall rate and F value of the word-level model in Chinese geographical names recognition are obviously improved, in which the F value is increased by 2.88. The value of F was increased by 26.41 when the rare words were included.
【作者单位】： 南京理工大学经济管理学院;计算机软件新技术国家重点实验室(南京大学);
【基金】：国家自然科学基金资助项目(71503124,71303120) 江苏省社会科学基金资助项目(15TQC03)
【分类号】：TP18;TP391.1
，
本文编号：2307210
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