So the problem I'm solving is this: I have a list of conversations of 3 messages each (for eg. "hi", " how are you", "remind me to fix this bug" is one conversation, and my problem will have many of these) and need to pick out the message that is most likely to be a task ("remind me to fix this bug" would be the message in the conversation above; "don't forget to commit your code" would be another in some other conversation). What kind of machine learning/NLP model could I use to relatively rank the messages in a conversation and "learn" across conversations? And what would my training data look like?
My thoughts: each data point in the training data is one conversation (a set of 3 messages) along with the message with the highest likelihood of being a "task." I was thinking of using a linear weighted function of features such as TF-IDF score. But how would I learn the weights?