Short Text Language Identification for Under Resourced Languages
This addresses the problem of identifying languages in short texts for under-resourced and similar languages, but it is incremental as it builds on existing methods and datasets.
The paper tackles language identification for short texts in under-resourced languages, specifically evaluating on 11 South African languages, and achieves results comparable to recent approaches on established test sets.
The paper presents a hierarchical naive Bayesian and lexicon based classifier for short text language identification (LID) useful for under resourced languages. The algorithm is evaluated on short pieces of text for the 11 official South African languages some of which are similar languages. The algorithm is compared to recent approaches using test sets from previous works on South African languages as well as the Discriminating between Similar Languages (DSL) shared tasks' datasets. Remaining research opportunities and pressing concerns in evaluating and comparing LID approaches are also discussed.