Towards a Deep Multi-layered Dialectal Language Analysis: A Case Study of African-American English

Dacon, Jamell

doi:10.48550/arxiv.2206.08978

Cited by 1 publication

(3 citation statements)

References 11 publications

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“…Recently, there has been a surge in NLP research for AAE. Studies have explored dependency parsing (Blodgett et al, 2018), POS-tagging (Dacon, 2022;Jørgensen et al, 2016), hate speech classification (Harris et al, 2022;Sap et al, 2019), automatic speech recognition (Koenecke et al, 2020;Martin and Tang, 2020), dialectal analysis (Blodgett et al, 2016;Dacon, 2022;Stewart, 2014) and feature detection (Masis et al, 2022;Santiago et al, 2022). Projects such as these rely heavily on large amounts of labeled data, however, little research is dedicated to optimizing the disambiguation and annotation process.…”

Section: Related Workmentioning

confidence: 99%

“…Internet corpora, particularly Twitter data, dominate the data used for NLP research on AAE (Deas et al, 2023;Dacon, 2022;Harris et al, 2022;Blodgett et al, 2016Blodgett et al, , 2018Stewart, 2014;Jones, 2015;Jørgensen et al, 2016;Koufakou et al, 2020). AAE is extremely pervasive online, however, it is important to recognize its use in other domains such as everyday speech.…”

Section: Related Workmentioning

confidence: 99%

“…African American English (AAE) is considered a low-resource language, facing the challenge of inadequate annotated data for training natural language processing (NLP) models. While efforts to enhance annotation have shown promise in improving NLP performance on AAE (Dacon, 2022;Masis et al, 2023), the process remains timeconsuming, expensive, and often demands expertise that is not always available (Larimore et al, 2021). Furthermore, AAE lacks access to the same host of language-specific NLP tools available to dominant varieties such as Mainstream American English (MAE).…”

Section: Introductionmentioning

confidence: 99%

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Leveraging syntactic dependencies in disambiguation: the case of African American English

Previlon,

Rozet,

Gowda

et al. 2024

Preprint

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African American English (AAE) has received recent attention in the field of natural language processing (NLP). Efforts to address bias against AAE in NLP systems tend to focus on lexical differences. Whenever the structural uniqueness of AAE is considered, the solution is often to remove or neutralize the differences. This work leverages knowledge about the unique morphosyntactic structures to improve automatic disambiguation of habitual and nonhabitual meanings of “be” in naturally produced AAE transcribed speech. Both meanings are employed in AAE but examples of Habitual be are rare in the already limited AAE data. Generally, representing contextual syntactic information improves semantic disambiguation of habituality. Using an ensemble of classical machine learning models with a representation of the unique POS and dependency patterns of Habitual be, we show that integrating syntactic information improves the identification of habitual uses of “be” by about 65 F1 points over a simple baseline model of n-grams, and as much as 74 points. The success of this approach demonstrates the potential impact when weembrace, rather than neutralize, the structural uniqueness of African American English.

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