LIDE: Language Identification from Text Documents
This addresses the problem of identifying languages in microblogging data for users needing automated language processing, but it is incremental as it matches rather than surpasses existing performance.
The paper tackled language identification from short text documents, achieving 95.12% accuracy on the DSL Shared Task 2015 dataset, which is comparable to the state-of-the-art of 95.54%.
The increase in the use of microblogging came along with the rapid growth on short linguistic data. On the other hand deep learning is considered to be the new frontier to extract meaningful information out of large amount of raw data in an automated manner. In this study, we engaged these two emerging fields to come up with a robust language identifier on demand, namely Language Identification Engine (LIDE). As a result, we achieved 95.12% accuracy in Discriminating between Similar Languages (DSL) Shared Task 2015 dataset, which is comparable to the maximum reported accuracy of 95.54% achieved so far.