Unsupervised Context-Sensitive Bangla Spelling Correction with Character N-gram

We propose an unsupervised context-aware spelling error detection and correction method for traditional Bangla written script. The context-aware capability is introduced by generating a representation of information from the context words and using the concept of cosine similarity of find the best candidate for the misspelled words. We used fast-text embedding concept of using character n-gram embedding. The character n-gram embedding is capable of generating embedding for unknown words, which is a capability missing in any conventional methods. We performed our experiments on Bangla language collected from popular newspapers, blogs, Bangla Wikipedia and Banglapedia. Our proposed method outperforms conventional methods.