Chinese organization name recognition is hard and important in natural language processing. To reduce tagged corpus and use untagged corpus,we presented combing Co-training with support vector machines (SVM) and condi...Chinese organization name recognition is hard and important in natural language processing. To reduce tagged corpus and use untagged corpus,we presented combing Co-training with support vector machines (SVM) and conditional random fields (CRF) to improve recognition results. Based on principles of uncorrelated and compatible,we constructed different classifiers from different views within SVM or CRF alone and combination of these two models. And we modified a heuristic untagged samples selection algorithm to reduce time complexity. Experimental results show that under the same tagged data,Co-training has 10% F-measure higher than using SVM or CRF alone; under the same F-measure,Co-training saves at most 70% of tagged data to achieve the same performance.展开更多
Combat training is a necessary requirement under the new situation to expand our military missions and tasks. For this principle of the requirements, it must not be mechanically rigid to be understood and implemented,...Combat training is a necessary requirement under the new situation to expand our military missions and tasks. For this principle of the requirements, it must not be mechanically rigid to be understood and implemented, we need to seriously understand and grasp the essence of the meaning. Overall, the primitive nature of the combat training, including realistic, confrontational, diversity and experimental features, can answer the question of what combat training is, why and how to do the fundamental issues from different angles, and explain the nature of the characteristics of combat training from general and universal of angle.展开更多
基金National Natural Science Foundations of China (No.60873179, No.60803078)
文摘Chinese organization name recognition is hard and important in natural language processing. To reduce tagged corpus and use untagged corpus,we presented combing Co-training with support vector machines (SVM) and conditional random fields (CRF) to improve recognition results. Based on principles of uncorrelated and compatible,we constructed different classifiers from different views within SVM or CRF alone and combination of these two models. And we modified a heuristic untagged samples selection algorithm to reduce time complexity. Experimental results show that under the same tagged data,Co-training has 10% F-measure higher than using SVM or CRF alone; under the same F-measure,Co-training saves at most 70% of tagged data to achieve the same performance.
文摘Combat training is a necessary requirement under the new situation to expand our military missions and tasks. For this principle of the requirements, it must not be mechanically rigid to be understood and implemented, we need to seriously understand and grasp the essence of the meaning. Overall, the primitive nature of the combat training, including realistic, confrontational, diversity and experimental features, can answer the question of what combat training is, why and how to do the fundamental issues from different angles, and explain the nature of the characteristics of combat training from general and universal of angle.
