Training a model is the process of executing an algorithm against a set of data, formulating the model, and then verifying the model. We may encounter situations where the text that needs to be processed is significantly different from what we have seen and used before. For example, using models trained with journalistic text might not work well when processing tweets. This may mean that the existing models will not work well with this new data. When this situation arises, we will need to train a new model.

To train a model, we will often use data that has been marked up in such a way that we know the correct answer. For example, if we are dealing with POS tagging, the data will have POS elements (such as nouns and verbs) marked in the data. When the model is being trained, it will use this information to create the model. This dataset is called a corpus.