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

Chloe Eggleston, Brendan O’Connor


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.
Anthology ID:
2022.wnut-1.4
Volume:
Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
38–50
Language:
URL:
https://aclanthology.org/2022.wnut-1.4
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis (Eggleston & O’Connor, WNUT 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.wnut-1.4.pdf
Code
 slanglab/tweetie_wnut2022
Data
Tweebank