Language identification is the task of automatically detecting the languages(s) written in a text or a document given, and is also the very first step of further natural language processing tasks. This task has been well-studied over decades in the past, however, most of the works have focused on long texts rather than the short that is proved to be more challenging due to the insufficiency of syntactic and semantic information. In this work, we present approaches to this problem based on deep learning techniques, traditional methods and their combination. The proposed ensemble model, composed of a learning based method and a dictionary based method, achieves 89.6% accuracy on our new generated gold test set, surpassing Google Translate API by 3.7% and an industry leading tool Langid.py by 26.1%. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/25126 |
| Date | January 2020 |
| Creators | Cui, Yexin |
| Contributors | Chen, Jun, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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