基于Transformer局部信息及语法增强架构的中文拼写纠错方法
针对中文拼写纠错,提出两种新的改进方法.其一,在Transformer注意力机制的基础上,添加高斯分布的偏置矩阵,用于提高模型对局部文本的关注程度,加强对错误文本中错误字词和周边文字的信息提取.其二,使用ON_LSTM模型,对错误文本表现出的特殊语法结构特征进行语法信息提取.实验结果表明,所提出的两种方法均能有效提高准确率和召回率,并且,将两种方法融合后的模型取得最高F1值....
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| Published in | 北京大学学报（自然科学版） Vol. 57; no. 1; pp. 61 - 67 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
北方工业大学信息学院,北京 100043
20.01.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0479-8023 |
| DOI | 10.13209/j.0479-8023.2020.081 |
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| Abstract | 针对中文拼写纠错,提出两种新的改进方法.其一,在Transformer注意力机制的基础上,添加高斯分布的偏置矩阵,用于提高模型对局部文本的关注程度,加强对错误文本中错误字词和周边文字的信息提取.其二,使用ON_LSTM模型,对错误文本表现出的特殊语法结构特征进行语法信息提取.实验结果表明,所提出的两种方法均能有效提高准确率和召回率,并且,将两种方法融合后的模型取得最高F1值. |
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| AbstractList | 针对中文拼写纠错,提出两种新的改进方法.其一,在Transformer注意力机制的基础上,添加高斯分布的偏置矩阵,用于提高模型对局部文本的关注程度,加强对错误文本中错误字词和周边文字的信息提取.其二,使用ON_LSTM模型,对错误文本表现出的特殊语法结构特征进行语法信息提取.实验结果表明,所提出的两种方法均能有效提高准确率和召回率,并且,将两种方法融合后的模型取得最高F1值. |
| Author | 王昊 段建勇 袁阳 |
| AuthorAffiliation | 北方工业大学信息学院,北京 100043 |
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| Author_FL | WANG Hao YUAN Yang DUAN Jianyong |
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| Author_xml | – sequence: 1 fullname: 段建勇 – sequence: 2 fullname: 袁阳 – sequence: 3 fullname: 王昊 |
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