Text-theme, the writing or speaking motive, is construed before creation at the basis of a writer＇s or speaker＇s background and typical cognitive structures. This paper attempts to explore text-theme in the Pearl Ha...Text-theme, the writing or speaking motive, is construed before creation at the basis of a writer＇s or speaker＇s background and typical cognitive structures. This paper attempts to explore text-theme in the Pearl Harbor address to the nation within the framework of appraisal theory founded by James Martin, et al., the excavation is centered on how the grammatical resources of appraisal can disclose the addressor＇s evaluation of the text-theme. The aim for the study is to testify the operationality and practicability of appraisal theory towards the exploration on text analysis. It is hoped that the theoretical and practical significance in the present exploration of text-theme can provide inspirations and novel insights in the realm of English language teaching and text analysis.展开更多
传统编目分类和规则匹配方法存在工作效能低、过度依赖专家知识、缺乏对古籍文本自身语义的深层次挖掘、编目主题边界模糊、较难实现对古籍文本领域主题的精准推荐等问题。为此,本文结合古籍语料特征探究如何实现精准推荐符合研究者需...传统编目分类和规则匹配方法存在工作效能低、过度依赖专家知识、缺乏对古籍文本自身语义的深层次挖掘、编目主题边界模糊、较难实现对古籍文本领域主题的精准推荐等问题。为此,本文结合古籍语料特征探究如何实现精准推荐符合研究者需求的文本主题内容的方法,以推动数字人文研究的进一步发展。首先,选取本课题组前期标注的古籍语料数据进行主题类别标注和视图分类;其次,构建融合BERT(bidirectional encoder representation from transformers)预训练模型、改进卷积神经网络、循环神经网络和多头注意力机制的语义挖掘模型;最后,融入“主体-关系-客体”多视图的语义增强模型,构建DJ-TextRCNN(DianJi-recurrent convolutional neural networks for text classification)模型实现对典籍文本更细粒度、更深层次、更多维度的语义挖掘。研究结果发现,DJ-TextRCNN模型在不同视图下的古籍主题推荐任务的准确率均为最优。在“主体-关系-客体”视图下,精确率达到88.54%,初步实现了对古籍文本的精准主题推荐,对中华文化深层次、细粒度的语义挖掘具有一定的指导意义。展开更多
文摘Text-theme, the writing or speaking motive, is construed before creation at the basis of a writer＇s or speaker＇s background and typical cognitive structures. This paper attempts to explore text-theme in the Pearl Harbor address to the nation within the framework of appraisal theory founded by James Martin, et al., the excavation is centered on how the grammatical resources of appraisal can disclose the addressor＇s evaluation of the text-theme. The aim for the study is to testify the operationality and practicability of appraisal theory towards the exploration on text analysis. It is hoped that the theoretical and practical significance in the present exploration of text-theme can provide inspirations and novel insights in the realm of English language teaching and text analysis.
文摘传统编目分类和规则匹配方法存在工作效能低、过度依赖专家知识、缺乏对古籍文本自身语义的深层次挖掘、编目主题边界模糊、较难实现对古籍文本领域主题的精准推荐等问题。为此,本文结合古籍语料特征探究如何实现精准推荐符合研究者需求的文本主题内容的方法,以推动数字人文研究的进一步发展。首先,选取本课题组前期标注的古籍语料数据进行主题类别标注和视图分类;其次,构建融合BERT(bidirectional encoder representation from transformers)预训练模型、改进卷积神经网络、循环神经网络和多头注意力机制的语义挖掘模型;最后,融入“主体-关系-客体”多视图的语义增强模型,构建DJ-TextRCNN(DianJi-recurrent convolutional neural networks for text classification)模型实现对典籍文本更细粒度、更深层次、更多维度的语义挖掘。研究结果发现,DJ-TextRCNN模型在不同视图下的古籍主题推荐任务的准确率均为最优。在“主体-关系-客体”视图下,精确率达到88.54%,初步实现了对古籍文本的精准主题推荐,对中华文化深层次、细粒度的语义挖掘具有一定的指导意义。
