Leveraging Syntactic Dependencies in Disambiguation: The Case of African American English

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3ALU4N6LYJ" target="_blank" >RIV/00216208:11320/25:LU4N6LYJ - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195933883&partnerID=40&md5=ea86e02720e8616016dd9ae1ee7c2621" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195933883&partnerID=40&md5=ea86e02720e8616016dd9ae1ee7c2621</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Leveraging Syntactic Dependencies in Disambiguation: The Case of African American English

Original language description

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 non-habitual 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 we embrace, rather than neutralize, the structural uniqueness of African American English. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Jt. Int. Conf. Comput. Linguist., Lang. Resour. Eval., LREC-COLING - Main Conf. Proc.

ISBN

978-249381410-4

ISSN

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e-ISSN

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Number of pages

13

Pages from-to

10403-10415

Publisher name

European Language Resources Association (ELRA)

Place of publication

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Event location

Torino, Italia

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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