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He says, she says. Pat says, Tricia says. How much reference resolution matters for entity extraction, relation extraction, and social network analysis

机译:他说,她说。帕特说,特里西娅说。实体提取,关系提取和社交网络分析有多少参考分辨率重要

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Anaphora resolution (AR) identifies the entities that pronouns refer to. Coreference resolution (CR) associates the various instances of an entity with each other. Given our data, our findings suggest that deduplicating and normalizing text data by using AR and CR impacts the literal mention, frequency, identity, and existence of about 75% of the entities in texts. Results are more moderate on the relation level: 13% of the links are modified and 8% are removed. Performing social network analysis on the relations extracted from texts leads to findings contrary to the results from corpus statistics: AR and CR cause different directions in the change of network analytical measures, AR alters these measures more strongly than CR does, and each technique identifies a different set of most crucial nodes. Bringing the results from corpus statistics and social network analysis together suggests that CR is more effective in normalizing entities, while AR is a more powerful technique for splitting up generic nodes into named entities with adjusted weights. Data changes due to AR and CR are qualitatively and quantitatively meaningful: the statistical properties of entities and relations change along with their identities. Consequently, the relational data represent the underlying social structure more truthfully. Our results can support analysts in eliminating some misinterpretations of graphs distilled from texts and in selected those nodes from social networks on which reference resolution should be performed.
机译:回指解析(AR)识别代词所指的实体。共指解析(CR)将实体的各种实例相互关联。根据我们的数据,我们的发现表明,通过使用AR和CR对文本数据进行重复数据删除和规范化会影响文字提及,频率,身份以及文本中约75%的实体的存在。在关系级别上,结果更为适度:修改了13%的链接,删除了8%的链接。对从文本中提取的关系进行社交网络分析会得出与语料统计结果相反的发现:AR和CR导致网络分析方法变化的方向不同,AR比CR更加强烈地改变了这些方法,每种技术都可以识别出不同的最关键节点集。将语料库统计数据和社交网络分析的结果综合在一起,表明CR在规范实体方面更有效,而AR是一种将权重节点调整为具有命名权的实体的更强大技术。由于AR和CR引起的数据变化在质量和数量上都是有意义的:实体和关系的统计属性及其身份也会发生变化。因此,关系数据更真实地代表了潜在的社会结构。我们的结果可以帮助分析人员消除对从文本中提取的图形的某些误解,并可以从社交网络中选择应该执行参考解析的那些节点中进行选择。

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