Post meeting summary by Weisi Duan

During the meeting, we first discussed about the standard LDA, including the representation and inference, learning. We discussed then about the labeled-LDA which is a supervised model that ties the labels of the documents to the hidden topics to obtain a distribution over vocabulary given labels. We discussed about the perks of this model such as adding prior as well as parameter tying. We also discussed about issues involving twitter data, eg. the dialog model by Alan Ritter et al.; the POS tagging within the twitter, such as why it is difficult; the API's and potential problems that they bring such as the samples are not intact dialogues. We discussed about researching a problem that is well formulated in the sense there is something concrete to evaluate, eg. the structured model for modeling twitter dialects can be used to predict the location of the speaker, which is a useful task.