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Recently I'm working on my course project, it's an android app that can automatically help fill form based on the user's voice. So here is one sample sentence: So what I want to do is let the app fill forms automatically, my forms have several fields: time(yesterday), location(MacDonald), cost(10 dollars), type(food). Here the "type" field will include food, shopping, transport, etc. What I have done is to split the sentence into several parts and parse it, so I can extract the time, location and cost. What I want to do is deduce the "type" field with some kind of machine learning model. So there should be some records in advance, input by user manually to train the model. After training, when new record comes in, I first extract the time, location and cost fields, and then calculate the type field based on the model. But I don't know how to represent the location field, should I use a dictionary to include many famous locations and use index to represent the location? If so, which kind of machine learning method should I use to model this requirement? |
