Spelling correction is the task of detecting and correcting spelling mistakes.
We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings.
This paper presents a finite state transducer approach to morphology analyser and generator for Malayalam language, an agglutinative, inflectional Dravidian language spoken by 38 million people, mainly by people from Kerala, India.
Phonetic similarity algorithms identify words and phrases with similar pronunciation which are used in many natural language processing tasks.
Inspired by the findings from the Cmabrigde Uinervtisy effect, we propose a word recognition model based on a semi-character level recurrent neural network (scRNN).
In this paper, we cast the CWS as a sequence translation problem and propose a novel sequence-to-sequence CWS model with an attention-based encoder-decoder framework.