Introduction of Semi-supervised Learning for Computational Linguistic
Introduction of Semi-supervised Learning for Computational Linguistic Creating sufficient labeled data can be very time-consuming. Obtaining the output sequences is not difficult: English texts are available in great quantity. What is time-consum Subsequent work in computational linguistics led to development of alternative algorithms for semisupervised learning, the algorithm of Yarowsky being a prominent example. These algorithms were developed specifically for the sorts of problems that arise frequently in computational linguistics: problems in which there is a linguistically correct answer, and large amounts of unlabeled data, but very little labeled data. Unlike in the example of acoustic modeling, classic unsupervised learning is inappropriate, because not just any way of assigning classes will do. The learning method is largely unsupervised, because most of the data is unlabeled, but the labeled data is indispensable, because it provides the only characterization of the linguist...