With the widespread use of Chinese globally, the number of Chinese learners has been increasing, leading to various grammatical errors among beginners. Additionally, as domestic efforts to develop industrial informati...With the widespread use of Chinese globally, the number of Chinese learners has been increasing, leading to various grammatical errors among beginners. Additionally, as domestic efforts to develop industrial information grow, electronic documents have also proliferated. When dealing with numerous electronic documents and texts written by Chinese beginners, manually written texts often contain hidden grammatical errors, posing a significant challenge to traditional manual proofreading. Correcting these grammatical errors is crucial to ensure fluency and readability. However, certain special types of text grammar or logical errors can have a huge impact, and manually proofreading a large number of texts individually is clearly impractical. Consequently, research on text error correction techniques has garnered significant attention in recent years. The advent and advancement of deep learning have paved the way for sequence-to-sequence learning methods to be extensively applied to the task of text error correction. This paper presents a comprehensive analysis of Chinese text grammar error correction technology, elaborates on its current research status, discusses existing problems, proposes preliminary solutions, and conducts experiments using judicial documents as an example. The aim is to provide a feasible research approach for Chinese text error correction technology.展开更多
针对中文文本检错纠错研究任务,提出了基于知识增强的自然语言表示模型(enhanced representation through knowledge integration, ERNIE)与序列标注结合的中文文本检错纠错模型。该模型由检错和纠错两部分组成,检错阶段ERNIE使用全局...针对中文文本检错纠错研究任务,提出了基于知识增强的自然语言表示模型(enhanced representation through knowledge integration, ERNIE)与序列标注结合的中文文本检错纠错模型。该模型由检错和纠错两部分组成,检错阶段ERNIE使用全局注意力机制进行词向量编码输入到BiLSTM-CRF序列标注模型中,双向长短期记忆网络(bi-directional long short-term memory, BiLSTM)提取上下文的信息进行拼接生成双向的词向量,再通过条件随机场(conditional random field, CRF)计算联合概率增加对邻近词标签的依赖性优化整个序列,从而解决标注偏置等问题给出的错误标注。纠错阶段根据检错模型输出的结果采用不同策略分类纠错,将标注为错字、缺字的错误使用ERNIE掩码语言模型和混淆集匹配进行预测,对多字、乱序错误直接纠正。实验结果表明,引入序列标注根据错误类型进行分类纠错有效提升了纠错率,在SIGHAN数据集上测试F1达到了81.8%。展开更多
文摘With the widespread use of Chinese globally, the number of Chinese learners has been increasing, leading to various grammatical errors among beginners. Additionally, as domestic efforts to develop industrial information grow, electronic documents have also proliferated. When dealing with numerous electronic documents and texts written by Chinese beginners, manually written texts often contain hidden grammatical errors, posing a significant challenge to traditional manual proofreading. Correcting these grammatical errors is crucial to ensure fluency and readability. However, certain special types of text grammar or logical errors can have a huge impact, and manually proofreading a large number of texts individually is clearly impractical. Consequently, research on text error correction techniques has garnered significant attention in recent years. The advent and advancement of deep learning have paved the way for sequence-to-sequence learning methods to be extensively applied to the task of text error correction. This paper presents a comprehensive analysis of Chinese text grammar error correction technology, elaborates on its current research status, discusses existing problems, proposes preliminary solutions, and conducts experiments using judicial documents as an example. The aim is to provide a feasible research approach for Chinese text error correction technology.
文摘针对中文文本检错纠错研究任务,提出了基于知识增强的自然语言表示模型(enhanced representation through knowledge integration, ERNIE)与序列标注结合的中文文本检错纠错模型。该模型由检错和纠错两部分组成,检错阶段ERNIE使用全局注意力机制进行词向量编码输入到BiLSTM-CRF序列标注模型中,双向长短期记忆网络(bi-directional long short-term memory, BiLSTM)提取上下文的信息进行拼接生成双向的词向量,再通过条件随机场(conditional random field, CRF)计算联合概率增加对邻近词标签的依赖性优化整个序列,从而解决标注偏置等问题给出的错误标注。纠错阶段根据检错模型输出的结果采用不同策略分类纠错,将标注为错字、缺字的错误使用ERNIE掩码语言模型和混淆集匹配进行预测,对多字、乱序错误直接纠正。实验结果表明,引入序列标注根据错误类型进行分类纠错有效提升了纠错率,在SIGHAN数据集上测试F1达到了81.8%。
