Affiliations: Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Tamil Nadu, India
Correspondence:
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Corresponding author. S. Sooraj, Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Tamil Nadu, India. E-mail: sudhakar.sooraj@gmail.com.
Abstract: Spell checking plays an important role in conveying correct information and hence helps in clear communication. Spell checkers for English language are well established. But in case of Indian languages, especially Malayalam lacks a well developed spell checker. The spell checkers that currently exist for Indian languages are based on traditional approaches such as rule based or dictionary based. The rich morphological nature of Malayalam makes spell checking a difficult task. The proposed work is a novel attempt and first of its kind that focuses on implementing a spell checker for Malayalam using deep learning. The spell checker comprises of two processes: error detection and error correction. The error detection section employs a LSTM based neural network which is trained to identify the misspelled words and the position where the error has occurred. The error detection accuracy is measured using the F1 score. Error correction is achieved by the selecting the most probable word from the candidate word suggestions.
Keywords: Malayalam, spell checker, deep learning, natural language processing, long short term memory
