Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis
Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis
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发表时间：
2022
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通讯作者：
Chloe Eggleston;Brendan T. O'Connor
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作者：
Chloe Eggleston;Brendan T. O'Connor
在本文中，我们利用社交媒体自然语言处理的最新进展，以获得最先进的社交媒体英语的句法依存分析结果。我们观察到3.4 UAS和4.0 LAS的性能增益对以前的国家的最先进的，以及非洲裔美国人和主流美国英语方言之间的差距较小。我们证明了计算社会科学的实用程序，这个解析器的任务，社会嵌入的实体属性分析：对于一个指定的实体，从解析器的丰富的语法推导出它的语义关系，并积累和比较它们在社会变量。我们对美国官员安东尼·福奇在COVID-19大流行期间的政治化观点进行了案例研究。
In this paper, we utilize recent advancements in social media natural language processing to obtain state-of-the-art syntactic dependency parsing results for social media English. We observe performance gains of 3.4 UAS and 4.0 LAS against the previous state-of-the-art as well as less disparity between African-American and Mainstream American English dialects. We demonstrate the computational social scientific utility of this parser for the task of socially embedded entity attribute analysis: for a specified entity, derive its semantic relationships from parses’ rich syntax, and accumulate and compare them across social variables. We conduct a case study on politicized views of U.S. official Anthony Fauci during the COVID-19 pandemic.