StanfordNER - training a new model and deploying a web service

Stanford NER is a named-entity recognizer based on linear chain Conditional Random Field (CRF) sequence models. This post details some of the experiments I've done with it, using a corpus to train a Named-Entity Recognizer: the features I've explored (some undocumented), how to setup a web service exposing the trained model and how to call it from a python script. Once Java is setup, you can run Stanford NER using one of the already trained models, which are distributed together with the zip file. Create a file with a sample sentence in english. Then, to apply the english.all.3class.distsim.crf.ser.gz to the sentence above, run the java command shown bellow: This section describes the basic steps to train your own NER model, from pre-processing the corpus (if needed), creating k-folds for cross-fold validation, defining the features to use, and running Stanford NER in evaluation mode.