Abstract: To prevent typos in electronic documents, quality checks are performed to identify portions of the document that have been misspelled. Major sources of false positives are words that have been transliterated from other languages into English. The quality control system described herein identifies transliterated words in electronic documents. The system may be trained to evaluate a set of words, and determine likelihoods that individual words in the set of words are transliterated (i.e., they are not native to the language and/or alphabet of the electronic document). In some embodiments, the model may be further configured to identify a language and/or alphabet that the individual word was transliterated from.

Abstract: A machine learning engine may correlate contextual information associated with a misspelling in a publication with a likelihood that the misspelling is intentional in nature. Training data may be generated by analyzing one or more past publication to identify misspellings and labeling the misspellings as intentional. A contextual indicators application may analyze the context in which intentional misspellings have been previously included within publication to identify indicators of future misspellings being intentional. A machine learning engine may use the training data and indicators to generate an intentional linguistic deviation (ILD) prediction model to determine whether a new misspelling is an intentional misspelling. The machine learning engine may also determine weights for individual indicators that may calibrate the influence of the respective individual indicators.