Work

Twitter Universal Dependency Parsing for African-American and Mainstream American English

Year: 2018

Type: conference-paper

Abstract: Due to the presence of both Twitterspecific 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 crossdomain 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

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/p18-1131

Authors: Su Lin Blodgett, Johnny Wei, Brendan O’Connor

Institution: University of Massachusetts Amherst

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Award: Graduate Research Fellowship Program (GRFP)