Topic distillation via sub-site retrieval

Topic distillation is one of the main information needs when users search the Web. Previous approaches for topic distillation treat single page as the basic searching unit, which has not fully utilized the structure information of the Web. In this paper, we propose a novel concept for topic distillation, named sub-site retrieval, in which the basic searching unit is sub-site instead of single page. A sub-site is the subset of a website, consisting of a structural collection of pages. The key of sub-site retrieval includes (1) extracting effective features for the representation of a sub-site using both the content and structure information, (2) delivering the sub-site-based retrieval results with a friendly and informative user interface. For the first point, we propose Punished Integration algorithm, which is based on the modeling of the growth of websites. For the second point, we design a user interface to better illustrate the search results of sub-site retrieval. Testing on the topic distillation task of TREC 2003 and 2004, sub-site retrieval leads to significant improvement of retrieval performance over the previous methods based on single pages. Furthermore, time complexity analysis shows that sub-site retrieval can be integrated into the index component of search engines.     

英文报刊杂志标题的修辞色彩

报刊杂志英语充满修辞, 尤以标题最为明显。在任何一种语言中, 修辞手法都是最常见的用于增强表达效果, 使表达更为生动活泼的手段之一。作为一种新的语言分支,报刊杂志英语适合使用各种英语修辞手法, 尤其是用在标题中。它们起着桥梁的作用, 使标题语言更加生动, 具体, 准确, 易懂, 从而使标题内容更加充实丰富。     

一种基于文本语义的网络敏感话题识别方法

网络已成为当今世界重要的信息载体,但是网络信息良莠不齐,对人们的生活造成了很多负面影响,因此,如何正确识别网络中的敏感话题,是当前网络舆情分析与监管的重要任务之一。本文以识别网络论坛中的敏感话题为目标,基于网络论坛文本在结构和表达上表现出的篇幅短、结构不完整、文字口语化等特性,将该类文本表示成基于向量空间模型的文本矩阵,并根据网络敏感话题具有先验知识和态度倾向性等特点,提出了基于倾向性词典的特征提取方法,可有效提高网络敏感话题识别的正确率,最后通过实验验证了这一改进的有效性,证实了本文的研究价值。     

Mining social influence in science and vice-versa: A topic correlation approach

There is no doubt that scientific discoveries have always brought changes to society. New technologies help solve social problems such as transportation and education, while research brings benefits such as curing diseases and improving food production. Despite the impacts caused by science and society on each other, this relationship is rarely studied and they are often seen as different universes. Previous literature focuses only on a single domain, detecting social demands or research fronts for example, without ever crossing the results for new insights. In this work, we create a system that is able to assess the relationship between social and scholar data using the topics discussed in social networks and research topics. We use the articles as science sensors and humans as social sensors via social networks. Topic modeling algorithms are used to extract and label social subjects and research themes and then topic correlation metrics are used to create links between them if they have a significant relationship. The proposed system is based on topic modeling, labeling and correlation from heterogeneous sources, so it can be used in a variety of scenarios. We make an evaluation of the approach using a large-scale Twitter corpus combined with a PubMed article corpus. In both of them, we work with data of the Zika epidemic in the world, as this scenario provides topics and discussions on both domains. Our work was capable of discovering links between various topics of different domains, which suggests that some of the relationships can be automatically inferred by the sensors. Results can open new opportunities for forecasting social behavior, assess community interest in a scientific subject or directing research to the population welfare.     

Web环境下的科技新闻主题挖掘——以国际极地考察领域为例

提出一种基于LDA主题模型的科技新闻主题分析方法,选取2009—2018年中、澳、英、美4国极地科考新闻数据,从主题类型和主题强度角度进行主题演化分析。在中文新闻中,极地测绘等主题的热度上升,极地冰川科考主题的热度下降;在英文新闻中,热门主题为极地冰川科考与极地海洋科考;其余主题热度相对稳定。研究结果表明,该方法可以有效识别科技新闻主题并揭示其演化趋势,可以有效改善网络环境下科技情报分析的自动化程度。     

话题追踪技术研究综述

海量的网络媒体信息使得人们在有限的时间内难以全面地掌握一些话题的信息,这样容易导致部分重要信息的遗漏。话题检测与追踪技术正是在这种需求下产生的。这种技术可以从庞大的信息集合中快速准确地获取人们感兴趣的内容。近几年,话题检测与追踪技术已成为自然语言处理领域热门的研究方向,它能把大量的信息有效地组织起来,并使用相关技术从中挖掘出有用的信息,用简洁有效的方式让人们了解一个事件或现象中所有细节以及它们之间的相关性。对话题跟踪的研究背景、相关概念、评测方法以及相关技术进行了综述,并总结了当前的相关技术。     

科技文献话题演化研究

提出一种研究话题演化的方法,利用LDA话题模型抽取科技文献的话题,通过计算话题的强度和特征词,研究话题的演化趋势。对NIPS论文集与ACL论文集进行实验,结果显示了机器学习领域以及计算语言学领域的一些发展状况,从而验证该方法的可行性。     

Strategic topic avoidance: an investigation of topic avoidance frequency,strategies used,and relational correlates

This research stresses the need to examine the relationship between topic avoidance and relational correlates (e.g., satisfaction and emotional closeness) from a message production theoretical perspective. Our approach—strategic topic avoidance—offers additional explanatory capabilities as the strategies with which interactants in close relationships avoid topics may be associated with perceptions of the relationship (after accounting for topic avoidance frequency). Moreover, relational correlates may also vary by the combination of overall topic avoidance frequency and certain topic avoidance strategies. The current research, therefore, assessed individuals' topic avoidance frequency levels and the frequency of using topic avoidance strategies in relation to satisfaction and closeness across three different relational types (i.e., significant others, mother–young‐adult, and father–young‐adult relationships). Results suggested that avoiding certain topics, such as current relational concerns, predicted levels of satisfaction and closeness across relationship types; however, cross‐relational differences also emerged. Strategies employed to avoid topics accounted for additional variance in satisfaction and closeness for relationships with significant others and mothers but not fathers. Analyses also demonstrated that overall topic avoidance frequency interacted with topic avoidance strategy use.     

重大科技基础设施对学科领域发展影响的计量评价研究——以对撞机为例

重大科技基础设施（简称重大设施）是具有前沿性、创新性、规模性及影响力的科学研究系统,在国家科技创新体系中占据重要地位。为了深刻认识重大设施的战略价值、充分发挥设施科研潜力、支撑未来设施的统筹布局,需要深入研究其对学科领域发展的影响作用。以专用型重大设施中的对撞机为研究对象,分析其论文成果的引文分布及主题强度演化,研究了重大设施对学科领域发展的影响过程,定义了不同影响类型,并形成了判别不同类型的三维战略坐标。在对比中、日、欧三方对撞机的成果产出和影响情况后,提出了相关政策建议。     

Exploring the effect of task instructions on topic beliefs and topic belief justifications: A mixed methods study

The purpose of this mixed methods study was to investigate whether task instructions that asked adolescents to evaluate the merit of both sides of a controversial issue would affect their topic beliefs and topic belief justifications after they read belief-consistent and belief-inconsistent information. In the quantitative phase, we conducted an experiment in which high school students (n = 45) were randomly assigned to one of four conditions and received their respective pre-reading task instructions. Quantitative analyses showed that task instructions affected topic beliefs and belief justifications. However, inspection of topic belief scores within each condition indicated that some individuals’ beliefs became weaker, whereas others’ became stronger. In the qualitative phase, we conducted interviews to explain why this occurred. The interview data revealed two distinct reader profiles: belief-reflection and belief-protection. The data sets were complementary: the quantitative data indicated group differences in topic beliefs and belief justifications, and the qualitative data allowed us to explain differences within and across groups.