1. A method for classifying a document in natural language processing using a natural language model stored in one or more data files, the method comprising: accessing one or more feature types from the data file, the one or more feature types each defining a data structure configured to access a tokenized sequence of the document and generate linguistic features from content within the tokenized sequence; performing a tokenizing operation of the document, the tokenizing operation configured to generate one or more tokenized sequences from the content within the document; generating a plurality of features for the document from the one or more tokenized sequences based on parameters defined by the one or more feature types; accessing a plurality of probabilities stored in the data file, each probability among the plurality of probabilities associated with a feature among the plurality of features and defining a pre-computed likelihood that said feature predicts a presence of absence of a label that the document is to be classified into; computing a prediction score indicating how likely the document is to be classified into said label, based on the plurality of probabilities; and classifying the document into said label based on comparing the prediction score to a threshold.