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Distinguishing intentional linguistic deviations from unintentional linguistic deviations

机译：区分故意语言偏差与非故意语言偏差

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摘要

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. The ILD prediction model may be deployed to analyze a new publication to identify a likelihood of the new misspelling being intentional.
机译：机器学习引擎可以将与出版物中的拼写错误相关联的上下文信息与拼写错误本质上是有意的可能性相关联。可以通过分析一个或多个过去的出版物来识别错误拼写并将错误拼写标记为故意来生成训练数据。上下文指示符应用程序可以分析先前在出版物中已包含有意的拼写错误的上下文，以标识未来有意的拼写错误的指示符。机器学习引擎可以使用训练数据和指示符来生成故意语言偏差（ILD）预测模型，以确定新的拼写错误是否是故意的拼写错误。机器学习引擎还可以确定各个指标的权重，这些权重可以校准各个指标的影响。可以部署ILD预测模型来分析新出版物，以识别新的拼写错误是故意的。

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