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dc.contributor.advisor陈毅东
dc.contributor.author万宇
dc.date.accessioned2018-12-05T01:47:48Z
dc.date.available2018-12-05T01:47:48Z
dc.date.issued2018-01-02
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/170486
dc.description.abstract语义相似度计算,也称语义相关度计算,是自然语言处理中最常见,也是最重要的工作之一。机器翻译、词义消歧等需要处理语义信息的任务,都与语义相似度计算有着紧密的联系。 评价一个语义学习算法是否优秀,往往通过将标准测试集的内容作为语义学习算法的输入,将算法结果与测试集结果进行一致性检验,一致性越高表示效果越好。因此,一个客观、公正的语义相关度标准测试集,可以更加全面地评价一个语义学习算法的优劣。本文的第一部分工作,是借助于传统的统计方法和认知神经科学实验方法,来构造语义相关度标准测试集。从部分词对的比较,以及与其他现有的标准测试集比较可以发现,本文构造的语义相关度标准测试集主要包括语义相似、语义相关...
dc.description.abstractSemantic similarity, also known as semantic relatedness, is one of the most common and important task in Natural Language Processing. Machine Translation, word sense disambiguation and other tasks that need to deal with semantic information are closely related to semantic similarity calculation. To evaluate a semantic learning algorithm is excellent or not, it is often to calculate the result upo...
dc.language.isozh_CN
dc.relation.urihttps://catalog.xmu.edu.cn/opac/openlink.php?strText=58743&doctype=ALL&strSearchType=callno
dc.source.urihttps://etd.xmu.edu.cn/detail.asp?serial=62740
dc.subject语义相关度
dc.subject词向量
dc.subject认知神经科学
dc.subject事件相关电位
dc.subject神经网络
dc.subjectsemantic relatedness
dc.subjectword vector
dc.subjectcognitive neuroscience
dc.subjectevent-related potentials(ERPs)
dc.subjectneural network
dc.title汉语词义相似新标准集构建与融合知网的词嵌入学习方法
dc.title.alternativeChinese Semantic Similarity Dataset Construction and Word Embedding Fused HowNet
dc.typethesis
dc.date.replied2017-05-19
dc.description.note学位:工学硕士
dc.description.note院系专业:信息科学与技术学院_模式识别与智能系统
dc.description.note学号:31520141153288


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