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

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.