RePAIR: Recommend political actors in real-time from news websites
RePAIR: Recommend political actors in real-time from news websites
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DOI：
10.1109/bigdata.2017.8258064
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
M. Solaimani;S. Salam;L. Khan;Patrick T. Brandt;Vito D'Orazio
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文献类型：
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作者：
M. Solaimani;S. Salam;L. Khan;Patrick T. Brandt;Vito D'Orazio
从新闻报道中提取政治事件的结构化表示是计算科学和社会科学的交叉点。传统的方法是使用基于字典的模式查找来识别潜在事件中涉及的参与者和动作。这种方法的一个关键复杂性是用新的参与者更新字典（例如，当新总统上任时）。目前，词典由人类管理，更新不频繁，成本很高。这意味着依赖于参与者字典的工具（例如，当字典中缺少参与者时，PETRIX）忽略事件。由于这些工具只使用句子的句法结构（例如，解析树等）对于它们的事件编码，缺失的参与者将产生无法捕捉实际政治互动的事件。为了克服这些问题，我们提出了一个框架修复推荐新的政治演员在实时的政治新闻文章与RSS提要有关的国家/国际政治在地球仪。该框架使用自动内容提取（ACE）方法识别句子的语义结构，并使用基于频率的演员排名算法来推荐多个时间窗口中最频繁的新政治演员。我们还建议从现有的CAMEO演员字典中共同发生的政治演员的作用，推荐的新演员的相关作用。此外，我们还整合了外部知识库（例如，Wikipedia）到我们的框架中，以捕捉现有参与者随着时间的推移而不断变化的角色，并为他们推荐新的角色。此外，我们认为PETRUNK和BBN ACCENT事件编码器的演员推荐，和基于图的演员角色推荐使用加权标签传播为基线，并与我们的框架进行比较。实验结果表明，我们的方法优于他们显着。
Extracting a structured representation of political events from news reports is at the intersection of the computational and social sciences. A traditional approach is to use dictionary-based pattern lookups to identify actors and actions involved in potential events. A key complication of this approach is updating the dictionaries with new actors (e.g., when a new president takes office). Currently, the dictionaries are curated by humans, updated infrequently, and at a high cost. This means that tools dependent on the actor dictionaries (e.g., PETRARCH) overlook events when actors are missing in the dictionary. Since these tools use only the syntactic structure of the sentence (e.g., parse tree, etc.) for their event coding, missing actors will generate events which fail to capture actual political interaction. To overcome these issues, we propose a framework RePAIR to recommend new political actors in real-time from the political news articles with RSS feeds related to national/international politics across the globe. The framework identifies semantic structure of a sentence using an Automatic Content Extraction (ACE) method and uses a frequency based actor ranking algorithm to recommend the most frequent new political actors over multiple time windows. We also suggest the associated role of recommended new actors from the role of co-occurred political actors in the existing CAMEO actor dictionary. Further we integrate an external knowledge base (e.g., Wikipedia) into our framework to capture the evolving roles of existing actors over time and recommend new roles for them. Furthermore, we consider PETRARCH and BBN ACCENT event coders for actor recommendation, and a graph-based actor role recommendation using weighted label propagation as baselines and compare them with our framework. Experimental results show our approaches outperform them significantly.