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Typos Correction in Overseas Chinese Learning Based on Chinese Character semantic Knowledge Graph

Journal of Data Analysis and Information Processing 2023年第2期11卷 200-216页

作者： Jing Xiong Xue Zhai Zhan Zhang Feng Gao School of Computer and Information Engineering Anyang Normal University Anyang China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education Anyang China School of Civil Engineering and Architecture Anyang Normal University Anyang China

In recent years, more and more foreigners begin to learn Chinese characters, but they often make typos when using Chinese. The fundamental reason is that they mainly learn Chinese characters from the glyph and pronunciation, but do not master the semantics of Chinese characters. If they can understand the meaning of Chinese characters and form knowledge groups of the characters with relevant meanings, it can effectively improve learning efficiency. We achieve this goal by building a Chinese character semantic knowledge graph (CCSKG). In the process of building the knowledge graph, the semantic computing capacity of HowNet was utilized, and 104,187 associated edges were finally established for 6752 Chinese characters. Thanks to the development of deep learning, OpenHowNet releases the core data of HowNet and provides useful APIs for calculating the similarity between two words based on sememes. Therefore our method combines the advantages of data-driven and knowledge-driven. The proposed method treats Chinese sentences as subgraphs of the CCSKG and uses graph algorithms to correct Chinese typos and achieve good results. The experimental results show that compared with keras-bert and pycorrector + ernie, our method reduces the false acceptance rate by 38.28% and improves the recall rate by 40.91% in the field of learning Chinese as a foreign language. The CCSKG can help to promote Chinese overseas communication and international education.

关键词： Chinese Character Meaning Knowledge Graph Typos Correction OpenHowNet semantic relevancy

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Query Expansion Based on semantics and Statistics in Chinese Question Answering System

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Wuhan University Journal of Natural Sciences 2008年第4期13卷 505-508页

作者： JIA Keliang PANG Xiuling LI Zhinuo FAN Xiaozhong School of Information Management Shandong Economic University Jinan 250014 Shandong China Department of Educational Science and Technology Weifang University Weifang 261061 Shandong China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China

In Chinese question answering system, because there is more semantic relation in questions than that in query words, the precision can be improved by expanding query while using natural language questions to retrieve documents. This paper proposes a new approach to query expansion based on semantics and statistics Firstly automatic relevance feedback method is used to generate a candidate expansion word set. Then the expanded query words are selected from the set based on the semantic similarity and seman- tic relevancy between the candidate words and the original words. Experiments show the new approach is effective for Web retrieval and out-performs the conventional expansion approaches.

关键词： Chinese question answering system query expansion relevance feedback semantic similarity semantic relevancy

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地理空间数据本质特征语义相关度计算模型

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地理 2016年第3期

作者：赵红伟诸云强杨宏伟罗侃

关联数据是跨网域整合多源异构地理空间数据的有效方式，语义丰富的关联是准确、快速发现目标数据的关键。根据地理空间数据在空间、时间、内容上的语义关系，提出地理空间数据本质特征语义相关度计算模型。通过构建本质特征的关联指标... 详细信息

关联数据是跨网域整合多源异构地理空间数据的有效方式，语义丰富的关联是准确、快速发现目标数据的关键。根据地理空间数据在空间、时间、内容上的语义关系，提出地理空间数据本质特征语义相关度计算模型。通过构建本质特征的关联指标体系，分层次逐级计算地理空间数据的语义相关度。与传统的语义相关度计算方式不同，以地理元数据为语料库，充分考虑地理空间数据的特点及空间、时间、内容在检索中不同的重要程度，分别采用几何运算、数值运算、词语语义相似度计算和类别层次相关度计算的方式，构建地理空间数据的语义相关度计算模型。该模型具有构建简单、适用于多源异构数据、充分结合了数学运算和专家经验知识等特点。实验表明：模型能够有效地计算地理空间数据本质特征的语义相关度，并具备一定的扩展性。

关键词：地理空间数据空间特征时间特征内容特征语义相关度 geospatial data spatial characteristic temporal characteristic content characteristic semantic relevancy

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