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Natural Language Processing to the Rescue? Extracting

Natural Language Processing to the Rescue? Extracting
课程网址: http://videolectures.net/icwsm2011_verma_awareness/  
主讲教师: Sudha Verma
开课单位: 科罗拉多大学
开课时间: 2011-08-18
课程语种: 英语
中文简介:
在大规模紧急情况下,大量数据是通过计算机介导的通信(CMC)生成的,这些数据很难手动筛选并组织成一幅连贯的画面。然而,有价值的信息是广播的,如果能够正确、快速地捕捉和分析,可以提供对时间和安全关键情况的有用见解。我们描述了一种自动识别通过Twitter传递的有助于态势感知的消息的方法,并解释了为什么这对在大规模紧急情况下寻求信息的人有利。我们收集了四个不同性质和规模的危机事件的Twitter消息,并构建了一个分类器,利用手动注释和自动提取的语言特征的组合,自动检测可能有助于情景感知的消息。我们的系统能够对有助于情境感知的推文进行分类,准确率达到80%以上。此外,我们还表明,为特定紧急事件开发的分类器在类似事件上表现良好。研究结果很有希望,有可能帮助公众在大规模紧急情况下收集和分析信息。
课程简介: In times of mass emergency, vast amounts of data are generated via computer-mediated communication (CMC) that are difficult to manually cull and organize into a coherent picture. Yet valuable information is broadcast, and can provide useful insight into time- and safety-critical situations if captured and analyzed properly and rapidly. We describe an approach for automatically identifying messages communicated via Twitter that contribute to situational awareness, and explain why it is beneficial for those seeking information during mass emergencies. We collected Twitter messages from four different crisis events of varying nature and magnitude and built a classifier to automatically detect messages that may contribute to situational awareness, utilizing a combination of hand annotated and automatically-extracted linguistic features. Our system was able to achieve over 80% accuracy on categorizing tweets that contribute to situational awareness. Additionally, we show that a classifier developed for a specific emergency event performs well on similar events. The results are promising, and have the potential to aid the general public in culling and analyzing information communicated during times of mass emergency.
关 键 词: 计算机介导; 态势感知; 自然语言处理
课程来源: 视频讲座网
数据采集: 2023-02-24:chenjy
最后编审: 2023-05-11:chenjy
阅读次数: 21