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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.wnut-1.4.pdf
- Code
- slanglab/tweetie_wnut2022
- Data
- Tweebank