Kontaktlinguistik 1996
DOI: 10.1515/9783110132649.1.6.709
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Linguistic discrimination

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Cited by 8 publications
(13 citation statements)
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“…Stories can be powerful tools of persuasion and expression, and richer methods that draw from both NLP and HCI can raise new questions and open up new directions. items, some of which have distinct semantic meanings, and may possess different syntactic structures/patterns than in Mainstream American English (MAE) (e.g., differentiating habitual be and non-habitual be usage) (Stewart, 2014;Dorn, 2019;Jones, 2015;Field et al, 2021;Bland-Stewart, 2005;Baugh, 2008;Blodgett et al, 2020;Labov, 1975). In particular, Green (2002) states that AAE possesses a morphologically invariant form of the verb that distinguishes between habitual action and currently occurring action, namely habitual be.…”
Section: Discussionmentioning
confidence: 99%
“…Stories can be powerful tools of persuasion and expression, and richer methods that draw from both NLP and HCI can raise new questions and open up new directions. items, some of which have distinct semantic meanings, and may possess different syntactic structures/patterns than in Mainstream American English (MAE) (e.g., differentiating habitual be and non-habitual be usage) (Stewart, 2014;Dorn, 2019;Jones, 2015;Field et al, 2021;Bland-Stewart, 2005;Baugh, 2008;Blodgett et al, 2020;Labov, 1975). In particular, Green (2002) states that AAE possesses a morphologically invariant form of the verb that distinguishes between habitual action and currently occurring action, namely habitual be.…”
Section: Discussionmentioning
confidence: 99%
“…Input I aint neva did dat befo Output (I, <PRP>), (aint, < VBP >), (neva, < NN >), (did, <VBD>)(dat, < JJ >), (befo, < NN >) To address these issues, we aim to empirically study predictive bias (see Swinton (1981) for definition) i.e., if POS tagger models make predictions dependent on demographic language features, and attempt a dynamic approach in data-collection of non-standard spellings and lexical items. To examine the behaviors of AAE speakers and their language use, we first collect variable (morphological and phonological) rules of AAE language features from literature (Labov, 1975;Bailey et al, 1998;Green, 2002;Bland-Stewart, 2005;Stewart, 2014;Blodgett et al, 2016;Elazar and Goldberg, 2018;Baugh, 2008;Green, 2014) (see Appendix C). Then, we employ 5 trained sociolinguist Amazon Mechanical Turk (AMT) annotators 3 who identify as bi-dialectal dominant AAE speakers to address the issue of lexical, semantic and syntactic ambiguity of tweets (see Appendix B for annotation guidelines).…”
Section: Aaementioning
confidence: 99%
“…Specifically, AAE is a regional dialect continuum that consists of a distinct set of lexical 1 A dialectal continuum previously known as Northern Negro English, Black English Vernacular (BEV), Black English, African American Vernacular English (AAVE), African American Language (AAL), Ebonics, and Non-standard English (Labov, 1975;Bailey et al, 1998;Green, 2002Green, , 2014Baugh, 2008;Bland-Stewart, 2005;King, 2020). It is often referred to as African American Language (AAL) and African American English (AAE).…”
Section: Introductionmentioning
confidence: 99%
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“…Linguistic discrimination has adversely affected the lives of marginalized populations for centuries, including racially marginalized groups in the United States. In spite of extensive research on linguistic discrimination (Baugh, 2008), many NLP systems inherit the linguistic biases that exist between humans. For example, preliminary studies into the performance of automatic speech recognition (ASR) systems uncovered a performance bias against African American speakers ( Tatman and Kasten, 2017;Dorn, 2019).…”
Section: Introductionmentioning
confidence: 99%