Automotive diagnosis typo correction using domain knowledge and machine learning

Text description of engineering diagnoses recorded during and after vehicle repair process plays an important role in root cause analyzing and vehicle maintenance. The fact that such text is unstructured, lack of grammar, has a lot of spelling errors and a large amount of self-invented domain specific terminologies introduces challenges and difficulties for automatic information retrieving and categorization. This paper presents our research in text mining in vehicle diagnostic applications. Specifically, an automatic typo correction system is proposed and implemented. We build multiple knowledge bases to detect and correct typos, and a neural network classifier to select good candidates for correcting typos. Experiment results show that our system outperforms state-of-art spell checking systems.