Text Sentiment Analysis Based on Multi-Layer Bi-Directional LSTM with a Trapezoidal Structure

Sentiment analysis,commonly called opinion mining or emotion artificial intelligence(AI),employs biometrics,computational linguistics,nat-ural language processing,and text analysis to systematically identify,extract,measure,and investigate affective states and subjective data.Sentiment analy-sis algorithms include emotion lexicon,traditional machine learning,and deep learning.In the text sentiment analysis algorithm based on a neural network,multi-layer Bi-directional long short-term memory(LSTM)is widely used,but the parameter amount of this model is too huge.Hence,this paper proposes a Bi-directional LSTM with a trapezoidal structure model.The design of the trapezoidal structure is derived from classic neural networks,such as LeNet-5 and AlexNet.These classic models have trapezoidal-like structures,and these structures have achieved success in the field of deep learning.There are two benefits to using the Bi-directional LSTM with a trapezoidal structure.One is that compared with the single-layer configuration,using the of the multi-layer structure can better extract the high-dimensional features of the text.Another is that using the trapezoidal structure can reduce the model’s parameters.This paper introduces the Bi-directional LSTM with a trapezoidal structure model in detail and uses Stanford sentiment treebank 2(STS-2)for experiments.It can be seen from the experimental results that the trapezoidal structure model and the normal structure model have similar performances.However,the trapezoidal structure model parameters are 35.75%less than the normal structure model.

基于语义增强模式链接的Text-to-SQL模型

为优化基于异构图编码器的Text-to-SQL生成效果,提出SELSQL模型。首先,模型采用端到端的学习框架,使用双曲空间下的庞加莱距离度量替代欧氏距离度量,以此优化使用探针技术从预训练语言模型中构建的语义增强的模式链接图;其次,利用K头加权的余弦相似度以及图正则化方法学习相似度度量图使得初始模式链接图在训练中迭代优化;最后,使用改良的关系图注意力网络(RGAT)图编码器以及多头注意力机制对两个模块的联合语义模式链接图进行编码,并且使用基于语法的神经语义解码器和预定义的结构化语言进行结构化查询语言(SQL)语句解码。在Spider数据集上的实验结果表明,使用ELECTRA-large预训练模型时,SELSQL模型比最佳基线模型的准确率提升了2.5个百分点,对于复杂SQL语句生成的提升效果很大。

A Brief Introduction of the Distinctive Styles in News Text

Newspaper is, to some extent, a mirror of our society, reflecting the latest change and development of the society.News text is a linguistic representation of the world. This paper is to briefly introduce the structure, writing and linguistic styles of news texts and thus to increase readers' awareness of the distinctive features of news texts.

基于STM的建筑物体打击事故致因关联分析

本文为系统分析建筑施工物体打击事故的产生机理,深入探索了事故调查报告中记录的主要致因类型及其联系。首先,选取2012—2021年国内136例物体打击事故调查报告,运用文本挖掘技术对事故报告进行预处理;然后,构建结构主题模型(STM),引入协变量建筑工种、击打物体和季节,对事故报告文本进行致因主题生成;再运用共现矩阵可视化直观展示主题间的关联关系;最后,分析各项主题与协变量之间的关联。研究结果表明:物体打击事故报告中呈现9项致因主题,其中人为因素占比最大。对于3个协变量,建筑工种中技术工人所面临的危险更多;一年中物体打击事故多发于夏季和春季;击打物体中原材料类和工程机械类物体易造成事故。根据事故致因的关联分析结果制定针对性的风险防范措施,可提高施工现场的安全管理水平。

Automatic Persian Text Summarization Using Linguistic Features from Text Structure Analysis

With the remarkable growth of textual data sources in recent years,easy,fast,and accurate text processing has become a challenge with significant payoffs.Automatic text summarization is the process of compressing text documents into shorter summaries for easier review of its core contents,which must be done without losing important features and information.This paper introduces a new hybrid method for extractive text summarization with feature selection based on text structure.The major advantage of the proposed summarization method over previous systems is the modeling of text structure and relationship between entities in the input text,which improves the sentence feature selection process and leads to the generation of unambiguous,concise,consistent,and coherent summaries.The paper also presents the results of the evaluation of the proposed method based on precision and recall criteria.It is shown that the method produces summaries consisting of chains of sentences with the aforementioned characteristics from the original text.

Cultural Influence on Text Structures──A Comparative Study of Chinese and German Routine Narratives

Culturalexchangesenablepeopletorecognizedifferencesbetweentextstructuresindifferentlanguages.Asearlyasthe60's,Kaplanalreadypointedouttheinfluencethatculturehadupontextstructures,butculturalfactorshavenotyetreceiveddueattentioninmanylinguisticwritings...

The effects of text structure, structure awareness and proficiency level on EFL learners＇ reading test performance

The present study probed into the effects of text structure, structure awareness and proficiency level on EFL learners＇ reading test performance. There are 112 college-level students participated in the experiment and their English proficiency belonged to distinct levels. The subjects＇ performance on the recall of two passages written in different types of structure was examined. Results of statistical indicate that text structure, structure awareness and proficiency level all have main effects on the subjects＇ reading performance. More specifically, two major findings emerged from the results of the investigation. One the one hand, text structures significantly affected the quantity but not the quality of the information recalled while proficiency level and structure awareness had significant impact on both the quantity and quality of information recalled. On the other hand, structure awareness was irrelevant to either text structure or proficiency level. The implications of the findings for teaching L2/FL reading were suggested.

THE TEXTS FORMERLY OWNED BY ST. PAUL PUBLIC LIBRARY AND PUBLISHED IN SET

In 1961 T. B. Jones and J. W. Snyder published 332 texts from many collections in the United States in their book Sumerian Economic Texts from the Third Ur Dynasty, a Catalogue and Discussion of Documents from Various Collections (Minneapolis)

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.

The Comparative Study of Two Translated Texts on Huangdirent Translating Styles o'n s Internal Classics——Simple Analysis on the Diffe Culture-Specific Lexicon, Figure of Speech and Four-Chinese-Character Structures

Huangdi's Internal Classics(Neijin) is one of the most important ancient medical classics, which plays far-reaching influence in medical field. More and more domestic and overseas scholars published their translated texts on Neijing. Due to the diversity of editions and different understanding, the translating styles and contents are widely different. This study will focus on the different translating styles on culture-specific lexicon、figure of speech and four-Chinese-character structures in Neijin.

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。

“双减”政策何以有效落地?基于“任务-主体-结构”的政策文本分析和对S省的调查

聚焦“双减”政策何以有效落地问题进行了两个新尝试。一是在研究范式上,从应然逻辑、实然状态和必然进路三个层面回应。二是在分析框架上,融合政策执行过程相关研究和结构化理论构建了突出目标导向、强化结构分析的“任务-主体-结构”政策执行过程模型,更契合我国复杂的政策执行环境。基于对“双减”政策文本和S省的落地实践分析发现:第一阶段的落地过程呈现出“运动式”治理的特征,但实际执行结构、落地条件和落地成效在内涵上远未达到设定目标。建议着力推动向“动员式”转型,以高质量教育体系建设为总抓手,有效回应家长对政策的价值需求。

转录与传习:西周诰类文献生成机制研究

西周的“诰”是受诰方对“王告”这一话语行为的尊称,并在“王告”落实为书面文献时,作为文体名词使用。由于下位者参与了“诰”类文献的整理写作,受诰者在话语权力关系中的被动地位被表达出来,并呈现在仪式话语转录为经典文本的传习过程中。传世诰类文献的核心文本为册命、赏赐仪式上所发布的原始诰辞;其附属的结构性成分为交代作诰历史背景的叙事导语,以及以“某人曰”为代表的结构性部件。“某人曰”在书面诰文中重复、平行地出现,有助于澄清和整肃话语主体,但也提示了诰文在传习过程中的可变异性。“王若曰”的插入位置,一定程度上反映了传习者对原始诰辞内容层次的理解,同时推动了诰辞转录为文本篇章的进程。

公文结构的理论研究述评与前瞻

当前的公文结构研究在形式要素结构、写作思维结构、写作结构模式、整体篇章结构等方面已取得显著成果。然而,由于多数研究集中在形式的静态考察和表层的宏观描写,导致结论的适用性和解释力有限。因此,未来的研究应重点关注内容信息结构,在篇章语言学视角下结合中国文件制度语境,运用跨学科研究方法,深入探讨公文结构的语言表达、话语结构、信息组织模式以及篇章结构系统要素之间的关系等问题。这将有助于形成科学的理论知识体系,为社会组织进行公文写作和交流沟通提供有力的理论支撑。

利用网络游记分析成都市旅游景区空间结构

从携程网爬取了2010—2019年成都市游记数据,构建旅游景区关键词文本共现网络,采用数量统计、空间分析、复杂网络分析等方法,挖掘成都市旅游景区游客到访的时空分异特征。结果表明,成都市旅游景区到访频率呈长尾分布,空间上形成以青城山—都江堰、金牛—武侯主城区为高热度中心的“双核摄动”格局;成都市旅游景区文本共现网络具有较高的集聚系数和较短的平均路径,等级圈层结构和马太效应较显著,热门景区对邻近热门景区的空间溢出效应显著,但对邻近低等级景区的带动效果有限;成都市旅游景区空间结构由条带状和团簇式向轴辐式和网络化方向转变,在区域旅游一体化发展中的作用日益显著。

融合词汇边界信息的合同实体识别方法

针对合同中实体表达形式复杂多变、识别粒度细的特点,及合同文本中实体较长问题,提出一种融合词汇边界信息的合同实体识别方法。利用预训练语言模型动态生成语义向量作为模型输入;运用相对位置编码对Transformer结构进行改进,使其在编码过程中融合词汇信息,进一步丰富语义特征;通过条件随机场(CRF)结构进行解码,得到输入序列的标签预测。实验结果表明,该方法可以有效确定合同文本中的实体边界,具有良好的泛化性能。

现代化背景下抽纸产品包装材质与工艺技术研究

受众群体对抽纸包装的满意程度影响抽纸产品的销售量,为适应和满足受众群体对抽纸包装的审美需求,从而增大抽纸产品销售量,提出现代化背景下抽纸产品包装材质与工艺技术研究,分别从图形与文字、色彩与材质、形态与结构三方面着手,分析现代化背景下抽纸产品包装材质与工艺要素,根据包装技术内涵,确定具体的包装材质与包装工艺,实现对抽纸产品的创新包装。随机选择100名路人作为研究对象,从颜色、图形、材质三方面实施调查,根据调查结果可知,大多数受众群体更喜欢依照现代化标准所制造出的抽纸包装,对该类型抽纸产品的购买欲望更强。因此依照现代化背景下的抽纸产品包装材质与工艺技术能够制造出更满足受众群体审美需求的抽纸包装,增加抽纸产品销售量。

基于事件表示和对比学习的深度事件聚类方法

针对现有深度聚类方法不考虑事件信息及其结构特点而难以有效划分事件类型的问题,提出一种基于事件表示和对比学习的深度事件聚类方法(DEC_ERCL)。首先,利用信息识别手段从非结构化文本中识别结构化的事件信息,避免冗余信息对事件语义的影响;其次,将事件的结构信息集成于自编码器中学习低维稠密的事件表示,并以此作为下游聚类划分的依据;最后,为有效建模事件之间的细微差异,在特征学习过程中加入多正例对比损失。在数据集DuEE、FewFC、Military和ACE2005上的实验结果表明,相较于其他深度聚类方法,所提方法在准确率和标准化互信息(NMI)评价指标上均表现更好;相较于次优的方法,DEC_ERCL的聚类准确率分别提升了17.85%、9.26%、7.36%和33.54%,表明了DEC_ERCL具有更好的事件聚类效果。

ST-组合Petri网的结构性质分析

本文提出ＳＴ－ 组合Ｐｅｔｒｉ网的概念，讨论了ＳＴ－ 组合Ｐｅｔｒｉ网对子网的结构性质保持问题，深入研究了ＳＴ－ 组合Ｐｅｔｒｉ网的结构活性、结构有界性、守恒性、可重复性、相容性、公平性．

公司治理结构与盈余管理研究——基于我国ST公司的经验证据

本文以我国ST上市公司为研究样本,从股权结构、董事会和监事会方面,对盈余管理与公司治理结构之问的关系进行了实证分析。结果表明,流通股与盈余管理无相关关系,股权集中度与盈余管理正相关,独立董事与盈余管理负相关。此外,由于我国股权结构相对比较集中,致使股权制衡度与盈余管理正相关,没有发挥其约束作用。这些结果表明,股权适度集中,有相对控股权的大股东的公司盈余管理较轻,治理效果较好。