Notice of Retraction Grammar Error Detection Tool for Medical Transcription using Stop Words Parts-of-Speech Tags Ngram Based Model

Ganesh B R, Deepa Gupta, Sasikala T


Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of APTIKOM's Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting


Medical transcription is the process of conversion of audio files, dictated by medical experts, to electronic data files in a predetermined format. The doctor ‘s thoughts are documented, covering medical procedures carried out on a patient starting from the time the patient enters the clinic or hospital, up until the ailment is treated. A grammar checker is an important asset to hospitals to scrutinize medical transcripts. The transcripts are important to track a patient’s medical history and need to be error free. The available existing tools are specifically designed to detect faulty grammatical constructs in the generic English language. It is important to improve the intelligence of a grammar checker in a relatively unknown domain and to improve the level of accuracy set by the existing tools which mostly rely on a set of non-exhaustive rulesets. These are the driving factors to propose a new approach to an old problem. Stop words are most commonly occurring words in any language. By exploiting the fact that stop words form the backbone of a sentence and by figuring out the common parts-of-speech tags which surround them,
a sentence’s grammatical structure can be better understood using statistical methods.


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