By Steven Abney
The swift development within the theoretical realizing of statistical and desktop studying equipment for semisupervised studying has made it tricky for nonspecialists to maintain so far within the box. supplying a wide, obtainable remedy of the speculation in addition to linguistic purposes, Semisupervised studying for Computational Linguistics bargains self-contained assurance of semisupervised equipment that incorporates heritage fabric on supervised and unsupervised learning.The e-book provides a quick historical past of semisupervised studying and its position within the spectrum of studying equipment ahead of relocating directly to speak about famous ordinary language processing equipment, akin to self-training and co-training. It then facilities on computing device studying strategies, together with the boundary-oriented equipment of perceptrons, boosting, aid vector machines (SVMs), and the null-category noise version. moreover, the publication covers clustering, the expectation-maximization (EM) set of rules, similar generative equipment, and contract tools. It concludes with the graph-based approach to label propagation in addition to a close dialogue of spectral methods.Taking an intuitive method of the cloth, this lucid ebook enables the applying of semisupervised studying tips on how to typical language processing and offers the framework and motivation for a extra systematic learn of laptop studying.
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Extra resources for Semisupervised Learning for Computational Linguistics (Chapman & Hall Crc Computer Science & Data Analysis)
For that reason, it is natural to discuss the applications at this point. It is hoped that the presentation of less familiar, but often mathematically sophisticated, methods in the remainder of the book will lead to their wider application in computational linguistics in the future. 1 Part-of-speech tagging Part-of-speech tagging has already been introduced. Instances are word occurrences in running text. An instance is represented as a collection of features. For part-of-speech tagging, the preceding word and following word are adequate for good performance.
Let X be the set of unlabeled instances where classifier confidence exceeds 1 − , that is, where yˆ maximizes c(y|x) and c(ˆ y |x) > 1 − . If c being a good estimate of p means that p(ˆ y |x) > 1 − for instances in X, then the proportion of erroneous predictions in X is less than , and we can take the predictions of the classifier on X at face value and increase the number of labeled instances for training, while keeping the proportion of labeling errors negligible. This reasoning makes several assumptions that are not necessarily true.
The probabilistic decision rule is then applied to the remaining unlabeled data, and its high-confidence predictions are accepted at face value and used to augment the statistics. Hearst, like Yarowsky, addresses the problem of word-sense disambiguation. She defines a decision rule based on certain statistics computed from labeled data, and then augments the statistics using unlabeled instances where the predictions of the decision rule are sufficiently confident. Neither Hearst nor Hindle & Rooth iterate the process of labeling and retraining.
Semisupervised Learning for Computational Linguistics (Chapman & Hall Crc Computer Science & Data Analysis) by Steven Abney