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        <title>Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis
%A Eggleston, Chloe
%A O’Connor, Brendan
%S Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F eggleston-oconnor-2022-cross
%X 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.
%U https://aclanthology.org/2022.wnut-1.4
%P 38-50

Markdown (Informal)

[Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis](https://aclanthology.org/2022.wnut-1.4) (Eggleston & O’Connor, WNUT 2022)

• Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis (Eggleston & O’Connor, WNUT 2022)

ACL

• Chloe Eggleston and Brendan O’Connor. 2022. Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis. In Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022), pages 38–50, Gyeongju, Republic of Korea. Association for Computational Linguistics.