AUTOMATIC EVALUATION AND VALIDATION OF TEXT MINING ALGORITHMS

摘要

In some embodiments, the disclosed subject matter involves comparing the results of natural language processing (NLP) of unstructured text to historical results for verification and validation of the NLP models/algorithms. The analysis uses statistical theory and practices to automatically monitor and validate the performances of the (NLP) algorithms on a periodic basis. Each unstructured text is run through one or more NLP algorithms and scored for relevance or contextual classification. Distribution of the scores is assumed to be Gaussian in nature so that a probability value (p-value) may be generated. When the p-value is below a threshold value, manual tagging may be initiated for the current time period to help retrain the models for better performance. Other embodiments are described and claimed.