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Stanford NER does have the 7 classes, but it is also possible to train an NER classifier using a more company-related dataset. This would allow more specific and relevant classes to be available. The app is meant to work in a single organization, on company-related issues. Swift can automatically perform text lookup mapping the text with the department, using the trained dataset. We can train the NER classifier on company messages that are already labeled, which will have different sentence structures. Stanford NER and others have their own train() method. The mapping is not supposed to be hardcoded. If there is an empty set, one solution is the app can make its best guess and inform the support team to confirm and further improve the model. Order usually doesnโt matter as much in NER since it is taking important pieces from the entire message.
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