通过新闻中未来事件提及与关联实体提取，提前预测社会动荡
Planned Event Forecasting using Future Mentions and Related Entity Extraction in News Articles
- 用主题建模和word2vec筛选相关新闻，结合命名实体识别定位关键信息
- 提出关联实体抽取方法，从众多提及中精准找出实际参与者
- 模型不依赖地理范围，适用于各地社会动乱预警
在印度等民主国家，民众自由表达诉求可能引发抗议、集会、游行等社会动荡，此类活动常无事先审批且具破坏性。由于抗议通常提前公告，可通过分析新闻中的预告信息实现事前预测。本文构建了一套系统，利用主题建模与word2vec筛选相关新闻，结合命名实体识别（NER）提取人物、组织、地点、日期等实体，并对未来的日期提及进行时间标准化处理。为解决大量实体提及中仅有少数真正参与事件的问题，本文提出‘关联实体提取’方法，聚焦真实参与者。所提模型具备地理无关性与通用性，可有效识别社会动荡事件的关键特征。
原文摘要 · Abstract (English)
In democracies like India, people are free to express their views and demands. Sometimes this causes situations of civil unrest such as protests, rallies, and marches. These events may be disruptive in nature and are often held without prior permission from the competent authority. Forecasting these events helps administrative officials take necessary action. Usually, protests are announced well in advance to encourage large participation. Therefore, by analyzing such announcements in news articles, planned events can be forecasted beforehand. We developed such a system in this paper to forecast social unrest events using topic modeling and word2vec to filter relevant news articles, and Named Entity Recognition (NER) methods to identify entities such as people, organizations, locations, and dates. Time normalization is applied to convert future date mentions into a standard format. In this paper, we have developed a geographically independent, generalized model to identify key features for filtering civil unrest events. There could be many mentions of entities, but only a few may actually be involved in the event. This paper calls such entities Related Entities and proposes a method to extract them, referred to as Related Entity Extraction.
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