Semantic text classification: A survey of past and recent advances

Automatic text classification is the task of organizing documents into pre-determined classes, generally using machine learning algorithms. Generally speaking, it is one of the most important methods to organize and make use of the gigantic amounts of information that exist in unstructured textual format. Text classification is a widely studied research area of language processing and text mining. In traditional text classification, a document is represented as a bag of words where the words in other words terms are cut from their finer context i.e. their location in a sentence or in a document. Only the broader context of document is used with some type of term frequency information in the vector space. Consequently, semantics of words that can be inferred from the finer context of its location in a sentence and its relations with neighboring words are usually ignored. However, meaning of words, semantic connections between words, documents and even classes are obviously important since methods that capture semantics generally reach better classification performances. Several surveys have been published to analyze diverse approaches for the traditional text classification methods. Most of these surveys cover application of different semantic term relatedness methods in text classification up to a certain degree. However, they do not specifically target semantic text classification algorithms and their advantages over the traditional text classification. In order to fill this gap, we undertake a comprehensive discussion of semantic text classification vs. traditional text classification. This survey explores the past and recent advancements in semantic text classification and attempts to organize existing approaches under five fundamental categories; domain knowledge-based approaches, corpus-based approaches, deep learning based approaches, word/character sequence enhanced approaches and linguistic enriched approaches. Furthermore, this survey highlights the advantages of semantic text classification algorithms over the traditional text classification algorithms.     

探析深度学习表征的一种新方法:社会认知网络特征(SENS)

近年来,深度学习在教育领域越来越受到重视,它被划分为三大维度:认知领域、人际领域和自我领域。已有的基于问卷、测试题和访谈等方式进行的学习效果分析,难以满足人们对于深度学习中的动态、过程和综合的分析需求。作为一种新的研究方法,社会认知网络特征(SENS)结合了社会网络分析(SNA)和认知网络分析(ENA)两种分析方法,能够很好地对深度学习的三个领域进行分析。通过对原有的SENS方法进行改进,提出了适合深度学习SENS方法的一般步骤,并对每个步骤进行详细的介绍;通过以两个典型案例展示如何利用SENS来促进深度学习,并将深度学习和在线协作数据分析相结合;提出了应用SENS的一些经验与建议,以期为今后研究深度学习提供新的思路和方法。     

引文网络中文献深度聚合方法与实证研究——以WOS数据库中XML研究论文为例

科学文献之间通过引用关系构成了特定研究主题的知识网络,其单向无回路的特征揭示了学科主题的知识结构和发展过程.本文以WOS数据库中XML研究论文所构成的引文网络为例,利用引文关系权重与文献节点权重确定核心文献,并在此基础上从阈值和权值“高地”两个角度对核心文献进行聚合.研究发现:文献核心程度的确定过程充分考虑了不同引用实质上的重要程度区别,据此计算得到的引文和文献节点权重能够准确反映文献的质量;阈值聚合能够迅速发现整个学科发展过程中最核心的文献和引文;权值“高地”聚合分析结果更为多样,并能弥补阈值聚合在揭示次重要子结构方面的不足,发现整个知识体系发展过程中丰富的研究维度.     

深度学习导向下小学数学交流策略的实践分析

知识型经济时代的到来使社会逐渐提升了对人才的要求,需具有一定的逻辑思维能力以及掌握一定的数学思想方法与数学交流能力。与传统教学方式相比,深度教学有着巨大的优势与良好的发展前景,不仅能够培养学生良好的逻辑思维与数学思想,还能在教学的同时贯彻素质教育,促进学生素质水平的提升。     

Deep learning questions can help selection of high ability candidates for universities

Selection of students for places at universities mainly depends on GCSE grades and predictions of A-level grades, both of which tend to favour applicants from independent schools. We have therefore developed a new type of test that would measure candidates’ ‘deep learning’ approach since this assesses the motivation and creative thinking that we look for in university students. We recruited 526 applicants to Oxford University and gave them a short commentary test and a learning style questionnaire. Specific deep learning approach questions correlated with results in the new test, and both predicted whether the candidate subsequently obtained a place at Oxford. Furthermore high scores on one open-ended commentary question, demanding arguments in favour of a case, produced a greater than 70% chance of obtaining a first class degree at the end of their course irrespective of the candidates’ type of school attended or GCSE scores. Candidates from State schools scored as well as those from Independent schools in both tests. Thus our test seemed to index candidates’ potential to succeed at a highly selective university, and might usefully be added to current selection procedures for such universities.     

High-intensity focused ultrasound with large scale spherical phased array for the ablation of deep tumors

et tissue by either moving single focal point or generating multiple loci simultaneously.     

论大英教学中汉译英技能的培养

运用乔姆斯基的转换生成语法理论,通过实例分析在大英教学中如何培养学生的汉译英技能,并指出了这种技能的重要性。     

论面向未来的公共图书馆包容性发展

本文提出了面向未来的公共图书馆包容性发展命题,分析了包容性增长与包容性发展的概念,论述了后全面小康时期公共图书馆实施包容性发展战略需要秉持的优质均衡、全程智能和协调共享的发展理念;阐述了公共图书馆包容性发展的路径图,提出了进一步将国家文化制度优势转化为文化治理效能、进一步发挥国家图书馆和省市公共图书馆的统筹协调功能、进一步把握新科技革命群体迭代的发展机遇等具体举措。     

视觉资源数字对象格式与精度研究

在文献调研、网络调研、实地走访和实验的基础上,对视觉资源数字对象常用的GIF、JPEG、JPEG2000、TIFF、DjVu等格式进行了分析,指出JPEG是视觉资源数字化过程中得到广泛应用的一种成熟格式,JPEG2000是一种较好的替代格式;DjVu格式不适合视觉资源的数字化;视觉资源数字化的最佳格式模式是采用600dpi全彩色TIFF格式进行扫描,再将扫描文件转换为300dpi的JPEG2000格式,采用DeepZoom技术进行网络呈现。     

空间的建构与认同——刘三姐文化深层底蕴的探讨之二

作为壮民族的文化代表之一的刘三姐文化之所以经久不衰,是与壮民族的文化空间建构与文化认同分不开的.刘三姐山歌文化现象的深层底蕴其实就是壮民族及其地区文化空间建构和认同的巨大隐喻,她的形成与流传过程就是壮族独特的"他者文化空间"、"身体文化空间"、"公共空间"、"认同空间"、审美及其他空间的建构和认同的历史过程.