Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis
Year: 2022
Type: other
Abstract: 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.
Source: Underline Science Inc.
DOI: 10.48448/sqtx-th07
Authors: International Committee for Computational Linguistics 2022, Eggleston, Chloe, O'Connor, Brendan
Institution: University of Massachusetts Amherst
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6966698202
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