Faking Errors to Avoid Making Errors: Very Weakly Supervised Learning for Error Detection in Writing
Jonas Sjöbergh, Kth Kod, Ola Knutsson · 2005
This paper describes a method to create a grammar checker “for free”. It requires no manual work, only unannotated text and a few basic NLP tools. The method used is to simply annotate a lot of errors in written text and train an off-the-shelf machine learning implementation to recognize such errors. To avoid manual annotation artificially created errors are used for training. Recall is comparable to other grammar checkers but precision is lower. Our method also complements traditional grammar checkers, i.e. they do not always find the same errors. The evaluation is performed on real errors. 1