Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis

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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Dependency Parsing Tweebank SuPar-BERTweet Labelled Attachment Score 83.4 # 1
Unlabeled Attachment Score 87.2 # 1


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