AI seminar: Semi-supervised multi-view HMMs learning for information extraction
Speaker: Zhenmei Gu
In information extraction (IE), training statistical models like HMMs (hidden Markov models) usually requires a considerably large set of labeled data. Such a requirement may not be easily met in a practical IE application. In this talk, we investigate how to adapt a fully supervised IE learner to a semi-supervised one so that the learner is able to make use of unlabeled data to help train a more robust model from very limited labeled data.