A scalable and adaptive method for finding semantically equivalent cue words of uncertainty

Scientific knowledge is constantly subject to a variety of changes due to new discoveries, alternative interpretations, and fresh perspectives. Understanding uncertainties associated with various stages of scientific inquiries is an integral part of scientists’ domain expertise and it serves as the core of their meta-knowledge of science. Despite the growing interest in areas such as computational linguistics, systematically characterizing and tracking the epistemic status of scientific claims and their evolution in scientific disciplines remains a challenge. We present a unifying framework for the study of uncertainties explicitly and implicitly conveyed in scientific publications. The framework aims to accommodate a wide range of uncertainty types, from speculations to inconsistencies and controversies. We introduce a scalable and adaptive method to recognize semantically equivalent cues of uncertainty across different fields of research and accommodate individual analysts’ unique perspectives. We demonstrate how the new method can be used to expand a small seed list of uncertainty cue words and how the validity of the expanded candidate cue words is verified. We visualize the mixture of the original and expanded uncertainty cue words to reveal the diversity of expressions of uncertainty. These cue words offer a novel resource for the study of uncertainty in scientific assertions.     

Developing a topic-driven method for interdisciplinarity analysis

This study explores the topic-based interdisciplinarity in the research domain of literacy. A text corpus of keywords was generated through a deep keyword generation model from abstracts of 346,387 articles published in 296 disciplines from 1917 to 2021. Dirichlet-Multinomial Regression topic modeling, interdisciplinarity indices, and network analysis were employed to analyze the collected corpus. Topic modeling uncovered 15 dominant research topics in the literacy field, as well as their up-and-down trends from 2000 to 2021. For each topic, keywords were then replaced with disciplines, and interdisciplinarity was measured using four indices: variety, balance, disparity, and diversity. Finally, the interdisciplinarity of each topic, connectivity between topics, and topic trends were comprehensively analyzed on the keyword co-occurrence network. Our methodology reaches beyond connectivity limited to a few disciplines and provides insight into the direction of collaboration between disciplines centered on a research domain. Moreover, the study's deep keyword generation model has methodological implications for forming a corpus spanning numerous disciplines as a bottom-up approach.     

“戏子”之痛——张恨水笔下的女性抒写微探

作为张恨水社会言情小说着重描写的对象之一,＂戏子＂的形象几乎贯穿于其小说创作的始终。作为社会一个特殊的群体,在艺术还只是供人消遣而尚未受到人们充分尊重的年代里,艺人的悲剧命运值得人们去反思。通过对《啼笑因缘》和《夜深沉》的个案分析,来探讨张恨水笔下的戏子形象及其造成其悲剧命运的自身因素,并由此来考察张恨水小说在暴露国民性弱点和鞭策民族劣根性、重塑民族魂方面所作出的努力与尝试。     

Should music evoke emotion?-Review on Deep Concern in New Horizon College English

Deep Concern in New Horizon College English (Reading and Writing Book1) describes a generation gap in an American family. In the text, the parents worry about their daughter’s listening to rock music for it may bring negative impact on her. Nowadays there is definitely music with unwholesome or depraved thoughts that may mislead listeners. Should people therefore refuse to listen to music or refuse the influence of music on emotion? The essay will focus on the issue to have a review on the relationship between emotion and music.     

校企深度融合的探索

"以校企合作体制机制创新为核心,全面深化校企合作"是未来中国高职教育发展的必由之路。由于多种复杂的因素,目前高职院校在校企合作中表现出诸多的矛盾和不适应。就校企深度融合的制约因素、途径和措施,结合本校实际情况进行分析和研究,旨在探索校企深度融合的发展之路。     

朱自清散文的艺术特色及成因探究

在中国现代文坛上朱自清作为＂散文圣手＂而被后来者广为赞誉。他的散文感情真、语言美、结构巧、道理深,被誉为现代＂白话美文的模范＂。探究其成因,源于＂厚爱＂,正是朱自清对家人、对人民、对祖国、对大自然、对艺术创作的厚爱铸就了他散文创作的艺术丰碑。     

基于通用搜索引擎的深层网络表面化方法研究

在现有相关研究的基础上,对基于通用搜索引擎的深层网络表面化方法的基本原理进行分析,对表单域取值范围的确定、查询处理、查询结果的超链接设置等与深层网络表面化相关的若干关键问题进行探讨。     

采用最小DFS的Deep Web结构化数据抽取

通过分析动态数据在其Web 页面中的展示特点,提出一个新的自动化、结构化数据抽取方法。首先基于DOM利用算法实现快速定位数据区,从而避免处理大量噪音数据;其次引入最小DFS编码来表示DOM子树,通过聚类对记录数据区进行区分;最后对少量样本页面训练学习生成抽取规则用于数据抽取。利用原型系统针对实际网站中的页面进行数据抽取,实验结果显示其拥有较高的准确性和效率。     

由民国时期的武术比赛引发的思考

通过对民国时期武术比赛、武术发展思想的分析和思考,并结合当今社会的发展形势,认为如何在文化安全、文化认同、道德建设和民族复兴等方面彰显自身的价值,展现自身的文化内涵,更好的为中华文化繁荣兴盛、中华民族的伟大复兴服务是武术今后发展面临的首要任务,也是武术发展所面临的重大历史契机和挑战。并进一步提出了确立"三维基点"、"一元综合"的发展设想。     

Deep Web信息抽取研究

针对Deep Web信息资源的利用问题，指出对其进行信息抽取的意义，分析对比在信息抽取过程中处理查询接口和抽取结构化数据这两个主要步骤所使用的技术，采用基于关键词查询和建立文档对象模型的方法对专利数据库进行抽取实验。通过分析实验结果，验证抽取方法的准确性，指出不足之处和解决的途径，以期达到充分利用Deep Web信息资源的目的。