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Articles by Muhammad Waqas Anwar
Total Records ( 2 ) for Muhammad Waqas Anwar
  Zili Zhang , Xuan Wang and Muhammad Waqas Anwar
  Arc segmentation is quite a challenging field in Graphics Recognition and the computation of the coordinates of centers and the radii of circular arcs is a crucial problem which has drawn much attention. Therefore this paper mainly discusses the application of circle fitting skill in scanning engineering drawings to determine correct coordinates of centers and radii. At first we should choose appropriate seed points and improve circle fitting algorithm of Instrumental Variable Estimator (IVE). Then we combine seed points and the improved IVE (IIVE) algorithm to calculate coordinates of centers and radii of circular arcs. In experimental section, the performance of IIVE and other two methods are compared by using classical experimental data and the coordinates of centers and radii are computed by employing the Arc Segmentation contest data. The results show that the proposed algorithm is very effective and efficient and the causes of the unsatisfactory results are analyzed.
  Xinxin Li , Xuan Wang and Muhammad Waqas Anwar
  Strategies of unlabeled data selection are important for semi-supervised learning of natural language processing tasks. To increase the accuracy and diversity of new labeled data, plenty of methods have been proposed, such as ensemble-based self-training, co-training and tri-training methods. In this paper, we propose a simple and effective semi-supervised algorithm for Chinese word segmentation and part-of-speech tagging problem which selects new labeled data agreed by two different approaches: character-based and word-based models. Theoretical and experimental analysis verifies that sentences with same annotation on both models are more accurate than those generated by single models and are suitable for semi-supervised learning as additional data. Experimental results on Chinese Treebank 5.0 demonstrate that our semi-supervised approach is comparable with the best reported semi-supervised approach which employs complex feature engineering.
 
 
 
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