行业期刊提升选题策划质量的途径——以《中国环境管理》的实践为例

【目的】 探讨行业期刊提升选题策划质量的途径。【方法】 结合《中国环境管理》的实践探索,对行业期刊快速积累选题资源的途径进行了梳理。【结果】 行业期刊应尽可能发挥选题的集聚效应、注重时效性和可读性。《中国环境管理》的稿件质量有较大改善,同时行业影响力逐步提升。【结论】 提升选题策划质量是塑造行业期刊品牌的有效途径。     

Improving topic disentanglement via contrastive learning

With the emergence and development of deep generative models, such as the variational auto-encoders (VAEs), the research on topic modeling successfully extends to a new area: neural topic modeling, which aims to learn disentangled topics to understand the data better. However, the original VAE framework had been shown to be limited in disentanglement performance, bringing their inherent defects to a neural topic model (NTM). In this paper, we put forward that the optimization objectives of contrastive learning are consistent with two important goals (alignment and uniformity) of well-disentangled topic learning. Also, the optimization objectives of contrastive learning are consistent with two key evaluation measures for topic models, topic coherence and topic diversity. So, we come to the important conclusion that alignment and uniformity of disentangled topic learning can be quantified with topic coherence and topic diversity. Accordingly, we are inspired to propose the Contrastive Disentangled Neural Topic Model (CNTM). By representing both words and topics as low-dimensional vectors in the same embedding space, we apply contrastive learning to neural topic modeling to produce factorized and disentangled topics in an interpretable manner. We compare our proposed CNTM with strong baseline models on widely-used metrics. Our model achieves the best topic coherence scores under the most general evaluation setting (100% proportion topic selected) with 25.0%, 10.9%, 24.6%, and 51.3% improvements above the second-best models’ scores reported on four datasets of 20 Newsgroups, Web Snippets, Tag My News, and Reuters, respectively. Our method also gets the second-best topic diversity scores on the dataset of 20Newsgroups and Web Snippets. Our experimental results show that CNTM can effectively leverage the disentanglement ability from contrastive learning to solve the inherent defect of neural topic modeling and obtain better topic quality.     

Topic Driven Adaptive Network for cross-domain sentiment classification

As a hot spot these years, cross-domain sentiment classification aims to learn a reliable classifier using labeled data from a source domain and evaluate the classifier on a target domain. In this vein, most approaches utilized domain adaptation that maps data from different domains into a common feature space. To further improve the model performance, several methods targeted to mine domain-specific information were proposed. However, most of them only utilized a limited part of domain-specific information. In this study, we first develop a method of extracting domain-specific words based on the topic information derived from topic models. Then, we propose a Topic Driven Adaptive Network (TDAN) for cross-domain sentiment classification. The network consists of two sub-networks: a semantics attention network and a domain-specific word attention network, the structures of which are based on transformers. These sub-networks take different forms of input and their outputs are fused as the feature vector. Experiments validate the effectiveness of our TDAN on sentiment classification across domains. Case studies also indicate that topic models have the potential to add value to cross-domain sentiment classification by discovering interpretable and low-dimensional subspaces.     

信息可视化与知识组织*

讨论信息可视化、知识组织的基本问题，并结合实例分析主题地图和本体的可视化。     

New journals for publishing medical case reports

Because they do not rank highly in the hierarchy of evidence and are not frequently cited, case reports describing the clinical circumstances of single patients are seldom published by medical journals. However, many clinicians argue that case reports have significant educational value, advance medical knowledge, and complement evidence-based medicine. Over the last several years, a vast number (∼160) of new peer-reviewed journals have emerged that focus on publishing case reports. These journals are typically open access and have relatively high acceptance rates. However, approximately half of the publishers of case reports journals engage in questionable or “predatory” publishing practices. Authors of case reports may benefit from greater awareness of these new publication venues as well as an ability to discriminate between reputable and non-reputable journal publishers.     

现代思维方式在体育科研选题中的应用

以介绍现代思维方式的基本特征，特别是创造性思维的特点为基础，结合多方面的知识和经验，探讨现代思维方式在体育科技研究选题中的应用。体育科技研究选题的方式可分为重复模仿、文献综述、移值嫁接及思索创新等几种主要形式。另外，还应充分注意两个关健环节，即课题的可行性与课题的价值及系统连续性。     

浅论民谣在当代的发展

中国民谣历史悠久，发展到当代，有了它新的内容与特色。本文试从时政谣、校园谣、手机谣三大方面对当代民谣的内容加以分类，分析其各自的特点，并从成因、传播、民谣自身的魅力三方面探索当代民谣的繁荣原因。     

可遇可求话选题——从编发《中药趣话》说开去

本文结合作者的工作经验,以《中药趣话》为例,分析做好选题策划值得借鉴的几点经验:顺应社会潮流和发展趋势,把握选题的时机;把出版社及编辑自身的优势结合起来;关注选题的读者对象与价值。     

文本可视化及其主要技术方法研究*

 文本可视化是通过对文本资源的分析,发现特定信息,并利用计算机技术将其以图形化方式呈现来的一种方法。通过分析文本可视化典型系统,分析现今的文本可视化的特点。并从基于词汇、基于篇章、基于时间序列、基于主题领域4种不同的文本可视化方式入手来分析其的主要技术方法。最后探讨文本可视化如何在信息环境下发挥作用。     

数字电视时代主题频道的发展

2004年以来,主题频道在数字电视付费系统建设的推动下成为众多节目供应商的选择,全国各个省市台分别推出了自己的主题频道。在数字付费电视的背景下,主题频道的运营状况如何?主题频道作为频道专业化在数字时代的一个表现,其发展背景、节目特色、营销模式发生了哪些变化?本文将对这些问题进行探讨。