Open-Set Language Identification
Table 2: One-class SVM classification results for each language using hashing-based feature vectorization based on character 4-grams. Each model is trained on 28k positive sentence, and tested against a set of 3,200 sentences positive sentences as well as all sentence from the other 9 classes (288,000 sentences).
['[BOLD] Language', '[BOLD] P', '[BOLD] R', '[BOLD] F1']
We trained a model for each language and ran it against the test fold. We observe almost perfect precision scores for all languages, and an average recall of 0.98, resulting in an average F-score of 0.989. These results clearly demonstrate the effectiveness of a one-class model for language identification.