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hi in this segment Im going to introduce the machine learning sequence model approach to named entity recognition and other kinds of information extraction tasks Im going to say a little bit about the structure of how you approach things and the features they use for that task and then in the next segment Im going to talk about the details of using Mac cementery models the sequence classifiers so if were going to use a sequence model for named entity recognition we need supervised training data what that means is we have examples of training documents where the words are labeled for what their entity class is so the steps that were going to have to go through is first of all collecting a representative set of training documents that contain entities and were interested in and the context were interest in them and then were going to go through each word and label each for its entity class or if its not if any entity class youll be labeled other which is normally denoted o the