Semi-supervised training

In this paper, a fault diagnosis scheme based on semisupervised classification (SSC) scheme is developed. In this scheme, new measurements collected from the plant are integrated with data observed under fault conditions to train the SSC models. The trained models are subsequently applied to new measurements for fault diagnosis. In comparison with supervised classifiers, the proposed scheme requires significantly fewer data collected under fault conditions to train the classifier.

11p minhxaminhyeu5 30-06-2019 8 0   Download

We consider the problem of NER in Arabic Wikipedia, a semisupervised domain adaptation setting for which we have no labeled training data in the target domain. To facilitate evaluation, we obtain annotations for articles in four topical groups, allowing annotators to identify domain-specific entity types in addition to standard categories. Standard supervised learning on newswire text leads to poor target-domain recall.

12p bunthai_1 06-05-2013 53 2   Download

We report an empirical study on the role of syntactic features in building a semisupervised named entity (NE) tagger. Our study addresses two questions: What types of syntactic features are suitable for extracting potential NEs to train a classifier in a semi-supervised setting? How good is the resulting NE classifier on testing instances dissimilar from its training data? Our study shows that constituency and dependency parsing constraints are both suitable features to extract NEs and train the classifier. ...

4p bunbo_1 17-04-2013 38 2   Download

The amount of unlabeled linguistic data available to us is much larger and growing much faster than the amount of labeled data. Semi-supervised learning algorithms combine unlabeled data with a small labeled training set to train better models. This tutorial emphasizes practical applications of semisupervised learning; we treat semi-supervised learning methods as tools for building effective models from limited training data. An attendee will leave our tutorial with 1.

1p hongphan_1 15-04-2013 50 1   Download

We present a simple and effective semisupervised method for training dependency parsers. We focus on the problem of lexical representation, introducing features that incorporate word clusters derived from a large unannotated corpus. We demonstrate the effectiveness of the approach in a series of dependency parsing experiments on the Penn Treebank and Prague Dependency Treebank, and we show that the cluster-based features yield substantial gains in performance across a wide range of conditions. ...

9p hongphan_1 15-04-2013 38 1   Download

This paper introduces a new training set condensation technique designed for mixtures of labeled and unlabeled data. It finds a condensed set of labeled and unlabeled data points, typically smaller than what is obtained using condensed nearest neighbor on the labeled data only, and improves classification accuracy. We evaluate the algorithm on semisupervised part-of-speech tagging and present the best published result on the Wall Street Journal data set.

5p hongdo_1 12-04-2013 30 3   Download

Semi-supervised word alignment aims to improve the accuracy of automatic word alignment by incorporating full or partial manual alignments. Motivated by standard active learning query sampling frameworks like uncertainty-, margin- and query-by-committee sampling we propose multiple query strategies for the alignment link selection task. Our experiments show that by active selection of uncertain and informative links, we reduce the overall manual effort involved in elicitation of alignment link data for training a semisupervised word aligner. ...

6p hongdo_1 12-04-2013 40 3   Download

Most attempts to train part-of-speech taggers on a mixture of labeled and unlabeled data have failed. In this work stacked learning is used to reduce tagging to a classification task. This simplifies semisupervised training considerably. Our prefered semi-supervised method combines tri-training (Li and Zhou, 2005) and disagreement-based co-training. On the Wall Street Journal, we obtain an error reduction of 4.2% with SVMTool (Gimenez and Marquez, 2004).

4p hongdo_1 12-04-2013 43 2   Download