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        <title>Twitter Universal Dependency Parsing for African-American and Mainstream American English</title>
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            <namePart type="given">Yusuke</namePart>
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    <abstract>Due to the presence of both Twitter-specific conventions and non-standard and dialectal language, Twitter presents a significant parsing challenge to current dependency parsing tools. We broaden English dependency parsing to handle social media English, particularly social media African-American English (AAE), by developing and annotating a new dataset of 500 tweets, 250 of which are in AAE, within the Universal Dependencies 2.0 framework. We describe our standards for handling Twitter- and AAE-specific features and evaluate a variety of cross-domain strategies for improving parsing with no, or very little, in-domain labeled data, including a new data synthesis approach. We analyze these methods’ impact on performance disparities between AAE and Mainstream American English tweets, and assess parsing accuracy for specific AAE lexical and syntactic features. Our annotated data and a parsing model are available at: http://slanglab.cs.umass.edu/TwitterAAE/.</abstract>
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%0 Conference Proceedings
%T Twitter Universal Dependency Parsing for African-American and Mainstream American English
%A Blodgett, Su Lin
%A Wei, Johnny
%A O’Connor, Brendan
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F blodgett-etal-2018-twitter
%X Due to the presence of both Twitter-specific conventions and non-standard and dialectal language, Twitter presents a significant parsing challenge to current dependency parsing tools. We broaden English dependency parsing to handle social media English, particularly social media African-American English (AAE), by developing and annotating a new dataset of 500 tweets, 250 of which are in AAE, within the Universal Dependencies 2.0 framework. We describe our standards for handling Twitter- and AAE-specific features and evaluate a variety of cross-domain strategies for improving parsing with no, or very little, in-domain labeled data, including a new data synthesis approach. We analyze these methods’ impact on performance disparities between AAE and Mainstream American English tweets, and assess parsing accuracy for specific AAE lexical and syntactic features. Our annotated data and a parsing model are available at: http://slanglab.cs.umass.edu/TwitterAAE/.
%R 10.18653/v1/P18-1131
%U https://aclanthology.org/P18-1131
%U https://doi.org/10.18653/v1/P18-1131
%P 1415-1425

Markdown (Informal)

[Twitter Universal Dependency Parsing for African-American and Mainstream American English](https://aclanthology.org/P18-1131) (Blodgett et al., ACL 2018)

• Twitter Universal Dependency Parsing for African-American and Mainstream American English (Blodgett et al., ACL 2018)

ACL

• Su Lin Blodgett, Johnny Wei, and Brendan O’Connor. 2018. Twitter Universal Dependency Parsing for African-American and Mainstream American English. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1415–1425, Melbourne, Australia. Association for Computational Linguistics.