This project is a part of the my transaction paper on Auto Correction and Sense Disambiguation in Transliterated Queries, which is currently under review. The project is also inspired by my following papers:

• Language Identification and Disambiguation in Indian Mixed-Script.
• Construction of a Semi-Automated model for FAQ Retrieval via Short Message Service.

• Can be retrained on a new dataset of well spelled queries in mixed languages such as Hindi-English, English-French, Hindi-Bengali, etc.
• No need of an annotated dataset, only need well spelled queries.
• Can be trained on smaller dataset - ~10K queries, giving reasonable performance.
• A trained mocdel on English corpus is provided, queries are taken from Yahoo webscope - 150K questions.
• The model is tested on a training corpus of 12K queries in English-Hindi mixed scripts (collected manually). The dataset will be made publically available as soon as the paper is accepted.

obj = auto_correct()
obj.run()

enter a query
hw to lrn pythn anddeeplearning eas ily
how to learn python and deep learning easily    11.2134873867

There are two parameters of the auto-corrector:

obj = auto_correct(retrain=,data=)

For retraining the model, set retrain = True and pass the queries as the other argument. The queries must be given in the following format:

queries=[]
queries = ['how to handle a 1.5 year old when hitting',
 'how can i avoid getting sick in china',
 'how do male penguins survive without eating for four months',
 'how do i remove candle wax from a polar fleece jacket',
 'how do i find an out of print book']

obj = auto_correct(retrain=True,data=queries)