We present a practical co-training method for bootstrapping statistical parsers using a small amount of manually parsed training material and a much larger pool of raw sentences. Experimental results show that unlabelled sentences can be used to improve the performance of statistical parsers. In addition, we consider the problem of bootstrapping parsers when the manually parsed training materia...

Accuracy is one of the basic principles of journalism. However, it is increasingly hard to manage due to the diversity of news media. Some editors of online news tend to use catchy headlines which trick readers into clicking. These headlines are either ambiguous or misleading, degrading the reading experience of the audience. Thus, identifying inaccurate news headlines is a task worth studying....

this study was an attempt to find out whether co-teaching model has statistically any significant impact on improving motivation and achievement of iranian young efl learners or not. the study was conducted at salehin english language department in tehran. to accomplish the purpose of this study, the researchers chose a sample of 91 participants which was reduced to 62 homogenous students throu...

This paper argues that co-teaching between native English teachers (NETs) and nonnative English teachers (NNETs) can contribute to the improvement of teaching quality of NETs, many of whom cannot achieve a desirable quality of teaching of English in Chinese primary schools largely due to lack of training in professional teaching. In this paper, four co-teaching models are examined—“One Teaching...

Since Fall 2004, we have been co-teaching a class on Grid computing to several NC institutions using the North Carolina Research and Education Network (NCREN). In this paper, we focus on a new teaching approach. In Fall 2008, we introduced a more integrated co-teaching or “pair-teaching” approach to the course. Our experiences are particularly relevant for a course taught in a distributed fashi...

It is often tedious and expensive to label large training data sets for learning-based object and scene recognition systems. This problem could be alleviated by semi-supervised learning techniques, which can automatically select more training samples from unlabel data for reducing the cost of labeling. In this paper, we proposed a multi-class co-training learning method of two different views f...

We investigate the application of the co-training learning algorithm on the sentence boundary classification problem by using lexical and prosodic information. Co-training is a semisupervised machine learning algorithm that uses multiple weak classifiers with a relatively small amount of labeled data and incrementally uses unlabeled data. The assumption in cotraining is that the classifiers can...

A comparison between co-training and self-training method for single-target regression based on multiples learners is performed. Data streaming systems can create a significant amount of unlabeled data which is caused by label assignment impossibility, high cost of labeling or labeling long duration tasks. In supervised learning, this data is wasted. In order to take advantaged from unlabeled d...