2026 EACL EACL 2026

Quantitative Lect Description: A Case Study of Lemko from the Field Data of 1920s-1930s

Abstract

AbstractWhile qualitative descriptions (in the form of reference grammars) and benchmarks for low-resource languages are becoming increasingly widespread, computational linguists do not often use quantitative methods to describe a new lect rather than a new model. This paper intends to close this lacuna. The case study is a Lemko text transcribed at the beginning of the twentieth century. Using morphosyntactic tagging and topic modelling, the study demonstrates areal influences and archaic features of the lect. Fine-grained evaluation significantly assists in identifying subtle patterns that are not readily apparent through traditional metrics such as accuracy score. The results highlight the necessity of a more detailed analysis of model performance, which may yield more linguistically significant results than a purely manual check. This information is present in the resulting dataset, which can be used for further investigation into the structural features of the Lemko lect.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Natural Language Processing
🧭 Keyword Pioneer — lect description
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Natural Language Processing, Speech & Audio

Authors