基于改进向量空间模型的话题识别与跟踪

话题识别与跟踪旨在发展一系列基于事件的信息组织技术,通过监测以实现对新闻媒体信息流中新话题的自动识别和已知话题的动态跟踪。文中提供一种利用改进的向量空间模型进行识别和跟踪的方法。没有使用传统向量空间模型中单个向量,而是按照语义将特征词划分为4个组(人物、时间、地点、内容)并形成4个向量空间。每个空间进行独立的权重计算和相似度计算。实验证明这些方法是有效的。     

基于规则和统计的中文自动文摘系统

自动文摘是自然语言处理领域里一个重要课题,本文在传统方法基础上提出了一种中文自动文摘的方法。在篇章结构分析里,我们提出了基于连续段落相似度的主题划分算法,使生成的文摘更具内容全面性与结构平衡性。同时结合了若干规则对生成的文摘初稿进行可读性加工处理,使最终生成的文摘更具可读性。最后提出了一种新的文摘评价方法(F-new-measure)对系统进行测试。系统测试表明该方法在不同文摘压缩率时,评价值均较为稳定。     

主题驱动的P2P分布式信息搜索机制研究

Peer—to—Peer（P2P）对于分布式文件共享具有很好的前景，但当前的P2P系统仍然缺乏有效的信息搜索机制．本文提出一种主题驱动的P2P信息搜索机制，通过对节点上的文档进行聚类获得全局主题，然后将包含有相似主题的节点组织到一起构成主题覆盖网络．当在P2P网络中进行信息搜索时，根据查询与主题的相关性路由查询，从而改善搜索效率．本文详细阐述了进行主题驱动搜索的索引结构、主题聚类方法、主题覆盖网络的构造与维护算法．在Chord上的模拟实验结果表明，主题驱动的P2P信息搜索机制可以减少信息搜索时的平均网络带宽和平均搜索路径长度，提高搜索的成功率．     

"研究性学习"课程初探

在我国,随着新一轮基础教育课程改革的全面推进,研究性学习课程应运而生.作为一门全新的课程,它在课程形态、课程实施、课程评价等方面都有别于传统课程.为了更好地理解和实施研究性学习课程,本文拟对研究性学习课程中的选题问题、教师指导问题、学生管理问题以及评价问题进行初步的探讨.     

研究生学位论文选题方法与论文质量的研究

为提高研究生学位论文质量，提出了论文选题、论文研究及论文答辩各环节中的改革措施．     

The ECHORD project proposals analysis – Research profiles,collaboration patterns and research topic trends

This paper investigates the research profiles, collaboration patterns and research topic trends which can be identified in the proposals submitted to the ECHORD (European Clearing House for Open Robotics Development) FP7 project. On a country level, clusters were identified and characterized by patterns of proposal production per inhabitant, score and international cooperation. Belgium and Sweden constitute a cluster characterized by high proposal production, with very high scores and extensive international collaboration. Belgium also excels from another cluster analysis, being as the only country where 100% of proposals involve industry–academia cooperation and obtain scores above 10. Other findings show that single partner proposals have significantly lower quality than multi-partner proposals but, on the other hand, the number of countries involved shows no influence on the quality of the proposals. Despite the high number of industrial participants present on the proposals, it is observed that they play secondary roles in the proposals, with a very low number projects leaded by companies. Also, it is observed that partnerships between research institutions (non-universities) are the most successful. Concerning topics of the proposals, the technology human–robot interface and the product vision robot for small-scale manufacturing are the most significant. Finally, the paper shows clusters of institutions extracted from the giant network of relations obtained from the ECHORD set of proposals.     

Analyzing market performance via social media: a case study of a banking industry crisis

Analyzing market performance via social media has attracted a great deal of attention in the finance and machine-learning disciplines.However,the vast majority of research does not consider the enormous influence a crisis has on social media that further affects the relationship between social media and the stock market.This article aims to address these challenges by proposing a multistage dynamic analysis framework.In this framework,we use an authorship analysis technique and topic model method to identify stakeholder groups and topics related to a special firm.We analyze the activities of stakeholder groups and topics in different periods of a crisis to evaluate the crisis’s influence on various social media parameters.Then,we construct a stock regression model in each stage of crisis to analyze the relationships of changes among stakeholder groups/topics and stock behavior during a crisis.Finally,we discuss some interesting and significant results,which show that a crisis affects social media discussion topics and that different stakeholder groups/topics have distinct effects on stock market predictions during each stage of a crisis.     

浅谈抗衰老产品的发展

自古以来人类为了探索生物体衰老的奥秘,已经开展了大量的研究,但是至今人类为什么会衰老,如何抵御衰老进程,依然是人们关注的永恒话题。而富足的生活和文明程度的提高,也为抗衰老化妆品市场的拓展提供了巨大的空间。     

Exploiting opinion distribution for topic recommendation in Twitter

The popular social networking service Twitter provides rapidly updated information and online trends, which enriches and benefits people's daily life. At the same time, how to find out the really interesting and relevant topics from the massive streams of tweets, to provide precise topic recommendation for users, becomes a challenging problem in the real world. Previous collaborative filtering methods give solutions to traditional recommendation tasks considering users' positive reviews to help recommend items. However, to the problem ‘what is interesting to whom’ in Twitter, positive opinions toward a topic do not imply that the user will be interested in it with high probability, for the user probably prefers to know those controversial topics or hot events with a large number of negative posts. In this paper, we exploit the characteristics of topical opinion distribution for improving the performance of recommendation. The experimental results on a real‐world Twitter dataset show that the proposed opinion‐distribution‐aware topic recommendation (ODA‐TR) approach outperforms the state‐of‐the‐art collaborative recommendation methods. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Lenzing公司Lyocell纤维专利分析

本文论述了Lyocell纤维的产业概况及生产工艺.以Lyocell纤维行业巨头奥地利Lenzing公司专利为研究目标,对其进行全面检索,分析其专利申请概况、布局情况、技术主题分布情况等,并在此基础上提出建议和措施以供国内相关企业借鉴.