An Unsupervised Morphological Criterion for Discriminating Similar Languages

Abstract

In this study conducted on the occasion of the Discriminating between Similar Languages shared task, I introduce an additional decision factor focusing on the token and subtoken level. The motivation behind this submission is to test whether a morphologically-informed criterion can add linguistically relevant information to global categorization and thus improve performance. The contributions of this paper are (1) a description of the unsupervised, low-resource method; (2) an evaluation and analysis of its raw performance; and (3) an assessment of its impact within a model comprising common indicators used in language identification. I present and discuss the systems used in the task A, a 12-way language identification task comprising varieties of five main language groups. Additionally I introduce a new off-the-shelf Naive Bayes classifier using a contrastive word and subword n-gram model (``Bayesline'') which outperforms the best submissions.

Tasks

Reproductions