What NLP Gets Wrong About Contact: Implications for Field Linguistic Evidence

AbstractField linguistics increasingly relies on computational tools to organize, analyze, and preserve linguistic data, yet the classificatory assumptions embedded in these tools are rarely examined. A pervasive assumption is that languages can be treated as discrete, genealogically defined units, with relatedness modeled as tree-structured descent. We argue that this assumption misrepresents linguistic evidence in contact-heavy regions and risks distorting the computational mediation of field linguistic data. Focusing on South Asia, we show that widely assumed boundaries—such as the Indo-Aryan–Dravidian divide—collapse in long-standing contact zones characterized by convergence, dialect continua, and institutional multilingualism. Through historically grounded case studies including Kannada–Telugu and Tamil–Malayalam, we demonstrate how convergence, script-mediated distance, and post-hoc standardization reshape how field data is segmented, compared, and interpreted when organized through genealogical labels. We argue that contact-aware, relational models of linguistic relatedness are necessary if NLP tools are to support, rather than distort, the documentation and analysis of linguistic diversity.