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自然语言处理如何来拯救?提取

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,通过计算机中介通信(CMC)生成大量数据,难以手动剔除并组织成连续图像。然而,如果正确和快速地捕获和分析,则可以广播有价值的信息,并且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传达的消息的方法,这些消息有助于形成情境,并解释为什么它对那些在大规模紧急情况下寻求信息的人有益。我们收集了来自不同性质和规模的四种不同危机的Twitter消息,并建立了一个分类自动检测可能有贡献的消息利用手工注释和自动提取的语言特征相结合的本地化意识。我们的系统能够在有助于态势感知的推文上实现超过80%的准确度。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。结果是有希望的,并有可能帮助一般公众剔除和分析在大规模紧急情况下传播的信息。
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; 数据; Twitter
课程来源: 视频讲座网
最后编审: 2020-07-29:yumf
阅读次数: 34