Since the exact segmentation guidelines likely varied across these systems-in particular, we found that Stanford CoreNLP often left off the title in names such as Mr. Smithand we didn't want to focus on these issues, we did not require exact matches of our name segmentation; instead, we consider the entire name as PERSON or LOCATION if any of the tokens were tagged as such (names with both tags were considered OTHER).
to build a training set, we pass through the corpus and each time we come across a common name in a particular document, we build a feature vector corresponding to all the contexts in that document, with the label taken from the clustering.