Lexicon

Text mining is data mining specifically for written data in natural language. The text mining process involves the use of algorithms and methods to extract valuable information from unstructured or semi-structured text data, identify new patterns, confirm existing patterns, or make predictions. The insights gained can be applied in many fields, such as science, marketing, customer service or finance.

Text spinning or article spinning is a technique aimed at changing a text to make it more appealing to a specific target audience. It involves replacing words, changing sentence structures, and inserting new words. The actual content of the text remains unchanged. This is relevant, for example, when creating new texts for search engine marketing (SEM), especially for search engine optimization (SEO or search engine marketing).

While training, a model learns from examples. Based on the examples, the model tries to predict an outcome (for example, filling in a cloze correctly) and compares its results with the real values at the end of each cycle. If the result is wrong, the underlying statistical model is adjusted and a new attempt is started. Usually, a training runs until the statistical model hardly changes anymore, i.e. the results become stable. This can be the case after a few minutes (classic machine learning) or weeks/months (deep learning on very large data sets).

A corpus used for training a model.