Visualization of Special Features in “The Tale of Genji” by Text Mining and Correspondence Analysis with Clustering

In this paper, visualization of special features in “The Tale of Genji”, which is a typical Japanese classical literature, is studied by text mining the auxiliary verbs and examining the similarity in the sentence style by the correspondence analysis with clustering. The result shows that the text mining error in the number of auxiliary verbs can be as small as 15%. The extracted feature in this study supports the multiple authors of “The Tale of Genji”, which agrees well with the result by Murakami and Imanishi [1]. It is also found that extracted features are robust to the text mining error, which suggests that the classification error is less affected by the text mining error and the possible use of this technique for further statistical study in classical literatures.

An Auto-Grading Oriented Approach for Off-Line Handwritten Organic Cyclic Compound Structure Formulas Recognition

Auto-grading,as an instruction tool,could reduce teachers’workload,provide students with instant feedback and support highly personalized learning.Therefore,this topic attracts considerable attentions from researchers recently.To realize the automatic grading of handwritten chemistry assignments,the problem of chemical notations recognition should be solved first.The recent handwritten chemical notations recognition solutions belonging to the end-to-end trainable category suffered fromthe problem of lacking the accurate alignment information between the input and output.They serve the aim of reading notations into electrical devices to better prepare relevant edocuments instead of auto-grading handwritten assignments.To tackle this limitation to enable the auto-grading of handwritten chemistry assignments at a fine-grained level.In this work,we propose a component-detectionbased approach for recognizing off-line handwritten Organic Cyclic Compound Structure Formulas(OCCSFs).Specifically,we define different components of OCCSFs as objects(including graphical objects and text objects),and adopt the deep learning detector to detect them.Then,regarding the detected text objects,we introduce an improved attention-based encoder-decoder model for text recognition.Finally,with these detection results and the geometric relationships of detected objects,this article designs a holistic algorithm for interpreting the spatial structure of handwritten OCCSFs.The proposedmethod is evaluated on a self-collected data set consisting of 3000 samples and achieves promising results.

Assessing trends in wildland-urban interface fire research through text mining: a comprehensive analysis of published literature

Research on fires at the wildland-urban inter-face(WUI)has generated significant insights and advance-ments across various fields of study.Environmental,agri-culture,and social sciences have played prominent roles in understanding the impacts of fires in the environment,in protecting communities,and addressing management challenges.This study aimed to create a database using a text mining technique for global researchers interested in WUI-projects and highlighting the interest of countries in this field.Author’s-Keywords analysis emphasized the dominance of fire science-related terms,especially related to WUI,and identified keyword clusters related to the WUI fire-risk-assessment-system-“exposure”,“danger”,and“vulnerability”within wildfire research.Trends over the past decade showcase shifting research interests with a growing focus on WUI fires,while regional variations highlighted that the“exposure”keyword cluster received greater atten-tion in the southern Europe and South America.However,vulnerability keywords have relatively a lower representation across all regions.The analysis underscores the interdisci-plinary nature of WUI research and emphasizes the need for targeted approaches to address the unique challenges of the wildland-urban interface.Overall,this study provides valu-able insights for researchers and serves as a foundation for further collaboration in this field through the understanding of the trends over recent years and in different regions.

Concept Association and Hierarchical Hamming Clustering Model in Text Classification

We propose two models in this paper. The concept of association model is put forward to obtain the co-occurrence relationships among keywords in the documents and the hierarchical Hamming clustering model is used to reduce the dimensionality of the category feature vector space which can solve the problem of the extremely high dimensionality of the documents' feature space. The results of experiment indicate that it can obtain the co-occurrence relations among key-words in the documents which promote the recall of classification system effectively. The hierarchical Hamming clustering model can reduce the dimensionality of the category feature vector efficiently, the size of the vector space is only about 10% of the primary dimensionality. Key words text classification - concept association - hierarchical clustering - hamming clustering CLC number TN 915. 08 Foundation item: Supporteded by the National 863 Project of China (2001AA142160, 2002AA145090)Biography: Su Gui-yang (1974-), male, Ph. D candidate, research direction: information filter and text classification.

A Heuristic Text Analytic Approach for Classifying Research Articles

Classification of research articles is fundamental to analyze and understand research literature. Underlying concepts from both text analytics and concept mining form a foundation for the development of a quantitative heuristic methodology, the Scale of Theoretical and Applied Research (STAR), for classifying research. STAR demonstrates how concept mining may be used to classify research with respect to its theoretical and applied emphases. This research reports on evaluating the STAR heuristic classifier using the Business Analytics domain, by classifying 774 Business Analytics articles from 23 journals. The results indicate that STAR effectively evaluates overall article content of journals to be consistent with the expert opinion of journal editors with regard to the research type disposition of the respective journals.

An Effective Concept Extraction Method for Improving Text Classification Performance

This paper presents anew way to extract concept that can beused to improve text classification per-formance (precision and recall). Thecomputational measure will be dividedinto two layers. The bottom layercalled document layer is concernedwith extracting the concepts of parti-cular document and the upper layercalled category layer is with findingthe description and subject concepts ofparticular category. The relevant im-plementation algorithm that dramatic-ally decreases the search space is dis-cussed in detail. The experiment basedon real-world data collected from Info-Bank shows that the approach is supe-rior to the traditional ones.

Viewing the Introduction of the Da Yan Pagoda from Functional Equivalence Theory

Translation is a trans-cultural communication activity which related to two languages. The main goal of translation is,no doubt, to establish a particular type of correspondence between the source text and the target text. The nature of the correspondence has been referred to"faithfulness"or"fidelity", or more predominantly, the notion of"equivalence".This issue attempts to analyze the brief introduction of the Da Yan Pagoda, which is a famous scene in Xian from the point of functional translation equivalence's view.

The Analysis of Dover Beach from the Perspective of New Criticism

Many of the literary works of Matthew Arnold reflect the contradictory psychology in the 19th century,of which Dover Beach ranks high..It touches the major concerns of Mid-Victorian writing.Since the readers have good acknowledgement of the extrinsic approach,to analyze this poem from a New Criticism point of view might better and objectively probe the central idea of it.

身份识别信息“使用”的界定纷争——基于美国加重身份盗窃犯罪的判例考察

非法使用个人信息行为的刑法规制问题是当前我国刑法研究的一大热点,但“使用”含义的多重性及极具争议性尚未引起学者们的关注。在最早单独制定身份信息犯罪立法的美国,对于如何理解身份识别信息的“使用”存在宽泛解释和限缩解释之间的严重对立。考察美国加重身份盗窃犯罪的判例,可以发现检方都极力主张宽泛解释,辩方则再三强调限缩解释的必要性,而各联邦巡回上诉法院的判决之间也存在严重分歧。美国最高法院通过杜宾案明确了对“使用”应当采取限缩解释的立场,并提出了“关键所在”标准,而该判决的协同意见则提出了对这一新标准的质疑,认为只有国会立法才能从根本上解决对“使用”的界定争议问题。美国司法实践中对身份识别信息“使用”的界定争议为我国非法使用个人信息的刑法规制研究提供了借鉴。

交替传译场域中两会记者会口译译者群体行为研究

通过阐述译者行为批评理论的描写性,可以推导出口译预设即口译译者行为的描写性。从译者行为批评理论的描写性及群体译者行为这一路径出发,在“文本-行为-社会”评价系统视域中,对口译译者群体行为进行探究,可以建构交替传译场域中的口译译者行为的描写性图式。此外,基于交替传译场域中口译译者群体行为的共性显征,可以得出口译“显文本”双重的动态建构过程及其描写图式,即“潜文本-预设行为-显文本-预设行为-社会”,这一反复在“文本”与“社会”之间的行为,是“语码-译入语文本”及“社会-译入语文本”双重的显文本生成过程。

AI生成式文学的创作试验与文本解读

随着ChatGPT技术迭代,AI生成式写作以及由此带来的一系列关于文学本质问题的思考,引发了研究者们的关注,但从目前的成果来看,对AI生成式写作的具体创作试验进行分析和文本解读的文章较少。故文章以首届“中文AI微小说大赛”的六篇获奖作品为例,研究其人机交互式写作的基本策略、当前AI生成式写作过程中的人机交互体验,通过解读AI生成式文学创作的具体文本,分析AI生成式写作带来的文本解读实践中存在的困境。

教育政策研究的理解诠释旨趣及其意蕴

教育政策研究的理解诠释旨趣强调探究教育政策所显示的意义、价值或信念。以理解诠释视角研究教育政策会发现:作为文本的教育政策具有耐久性和距离性,作为话语的教育政策具有建构性和实践性;教育政策具有效果历史意识和历史情境性,与权力具有互构性。教育政策研究者们应充分意识到:教育政策研究对象不只是客观事实,更是充满意义的世界;教育政策研究中不仅要注意时空和社会文化脉络对教育政策的影响,还要关注教育政策与社会问题、社会主体身份的关系;教育政策研究者应具备视域融合的视角,注意教育政策参与的民主性。显然,教育政策研究的理解诠释取向,能够为教育政策研究提供新的视角、问题意识和方法,有助于探明教育政策意义及其传递过程。

基于多模态阐释学的《江格尔》史诗传播与传承形态转变

《江格尔》作为我国三大史诗之一,在中国史诗学研究中具有十分重要的地位。史诗《江格尔》不仅以传统的演唱方式传承,还以印刷文本、图像文本、音视频文本、影视文本、雕塑画廊和文物遗址等多种形式加以传承和传播,不断实现着史诗《江格尔》的创造性转化和创新性发展,以适应新时代弘扬中华优秀传统文化的新要求。史诗《江格尔》这些多种传承和传播方式,构成了其作为中华优秀传统文化遗产的整体存在形态。基于“双创”理念,我们从阐释学研究维度就英雄史诗《江格尔》多模态传承与传播加以理论阐述,指出每一种史诗传承传播形态都有其存在的合理性和必要性,是新时代优秀文化遗产与时俱进的发展趋势,需要客观、科学和辩证的阐述和解读。我们要挖掘其深厚的文化内涵和时代价值,积极为新时代民间文艺学学科体系、学术体系和话语体系的建设贡献一份力量。

基于标签概念的多标签文本分类方法

多标签文本分类是自然语言处理中重要且具有挑战性的任务之一。现有的方法注重文本表示学习,关注文本内部信息预测所属标签,忽略了属于某一标签的全体实例中共享的关键信息。鉴于此,本文提出一种基于标签概念的多标签文本分类方法:利用词频和潜在狄利克雷分布(latent Dirichlet allocation,LDA)方法从训练集全体实例中抽取各标签所对应的关键词,接着采取与文本编码相同方式对关键词编码,获得标签概念表示。在训练和预测过程中,检索与文本表示最相似的标签概念辅助分类,增加标签概念表示与文本表示的对比损失,使文本编码过程中能充分学习全局的标签概念信息。将本文方法嵌套在常用的多标签文本分类模型上进行实验,结果表明该方法有效提高了相应模型的性能。

融合语义解释和DeBERTa的极短文本层次分类

文本层次分类在社交评论主题分类、搜索词分类等场景中有重要应用,这些场景的数据往往具有极短文本特征,体现在信息的稀疏性、敏感性等中,这对模型特征表示和分类性能带来了很大挑战,而层次标签空间的复杂性和关联性使得难度进一步加剧。基于此,提出了一种融合语义解释和DeBERTa模型的方法,该方法的核心思想在于:引入具体语境下各个字词或词组的语义解释,补充优化模型获取的内容信息;结合DeBERTa模型的注意力解耦机制与增强掩码解码器,以更好地把握位置信息、提高特征提取能力。所提方法首先对训练文本进行语法分词、词性标注,再构造GlossDeBERTa模型进行高准确率的语义消歧,获得语义解释序列;然后利用SimCSE框架使解释序列向量化,以更好地表征解释序列中的句子信息;最后训练文本经过DeBERTa模型神经网络后,得到原始文本的特征向量表示,再与解释序列中的对应特征向量相加,传入多分类器。实验遴选短文本层次分类数据集TREC中的极短文本部分,并进行数据扩充,最终得到的数据集平均长度为12词。多组对比实验表明,所提出的融合语义解释的DeBERTa模型性能最为优秀,在验证集和测试集上的Accuracy值、F1-micro值、F1-macro值相比其他算法模型有较大的提升,能够很好地应对极短文本层次分类任务。

基于领域概念图的航天新闻自动摘要模型

互联网海量的航天新闻中隐含着大量航天情报信息,对其进行理解与压缩是提高后续情报分析效率的基础。然而通用的自动摘要算法往往会忽略很多航天领域关键信息,且有监督自动摘要算法需要对领域文本进行大量的数据标注,费时费力。因此,提出一种基于领域概念图的无监督自动摘要(DCG-TextRank)模型,利用领域术语辅助引导图排序,提高模型对领域文本的理解力。该模型分3个模块:领域概念图生成、图权重初始化、图排序及语义筛选。根据句向量相似度和领域术语库,将文本转换为包含句子节点和领域术语节点的领域概念图;根据航天新闻文本特征初始化领域概念图权值;采用TextRank模型对句子进行排序,并在语义筛选模块通过图节点聚类及设置摘要语义保留度的方法改进TextRank的输出,充分保留文本的多语义信息并降低冗余。所提模型具有领域可移植性,且实验结果表明:在航天新闻数据集中,所提模型相比传统TextRank模型性能提升了14.97%,相比有监督抽取式文本摘要模型BertSum和MatchSum性能提升了4.37%~12.97%。

融合异质图表示学习与注意力机制的可解释论文推荐

学术论文推荐旨在为研究人员从海量学术资源中快速筛选出感兴趣的论文。现有论文推荐方法主要基于论文标题等文本内容和引用关系等进行推荐,使得蕴含丰富语义的多源学术信息的表示学习不够充分,制约了推荐准确度的进一步提升。同时,当前方法往往关注论文推荐的准确性,而忽略了可解释性,降低了论文推荐系统的可信度和用户满意度。为解决上述问题,本文提出了一种融合异质图表示学习与注意力机制的可解释论文推荐方法,该方法能够有效利用异质学术图中的语义信息,为推荐结果提供文本解释说明。具体来说,首先,提出了一种基于异质图表示学习与注意力机制的论文推荐模型,融合多源学术信息来构建语义丰富的异质学术图,并利用注意力机制学习不同节点和元路径的重要性,以获得更准确的节点表示。其次,提出了一种基于特征的文本解释生成模型,该模型将可解释文本生成方法引入论文推荐场景,能够在为作者提供推荐列表的同时生成文本解释,以告知其推荐缘由,从而提高论文推荐的可解释性。最后,构建了一个包含论文元数据、特征词、引用上下文的学术数据集,基于该数据集的对比实验结果表明,本文提出的基于异质图表示学习与注意力机制的论文推荐模型推荐准确度更高,解释生成模型能够为论文推荐结果提供质量较高的可解释文本说明。

数字化信息披露与资本市场定价——基于投资者信息解读的视角

作为经济发展的晴雨表,资本市场在赋能实体经济转型升级过程中发挥着重要作用。文章以我国沪深A股上市公司为研究对象,从投资者信息解读的视角考察了企业数字化信息披露对资本市场定价的影响。结果表明:企业数字化信息披露能够提高企业的市场估值,但该正向市场反应仅表现于长期;投资者信息解读在企业数字化信息披露与资本市场定价之间发挥了部分中介效应。进一步分析表明,在信息披露的真实性较高、投资者信息能力较低和信息需求较高时,企业数字化信息披露的长期市场反应更加显著,而在信息披露的真实性较低、投资者信息能力较低和信息需求较高时,投资者信息解读的中介效应更加显著。