On the basis of the traditional naive bayesian classification algorithm that just considered character of Chinese person name, we brought person name's up and down boundary words in it. In order to overcome the di...
详细信息
ISBN:
(纸本)9781479941711
On the basis of the traditional naive bayesian classification algorithm that just considered character of Chinese person name, we brought person name's up and down boundary words in it. In order to overcome the difficulty of boundary defining, we counted Chinese name's character frequency and boundary templates' frequency from tagged corpus. Then these recognized person names are used to match the missed occurrence in the text. The method is easy and the final result is good. Experimental results show that the F-value for recognition of Chinese person name was increased.
In the online learning environment, identifying learners' behaviors in the learning process can help them improve their learning effect autonomously. Firstly, we use K-Means algorithm to cluster the learner's ...
详细信息
ISBN:
(纸本)9781538684313
In the online learning environment, identifying learners' behaviors in the learning process can help them improve their learning effect autonomously. Firstly, we use K-Means algorithm to cluster the learner's help-seeking behavior data to get the classification label of the learner's help-seeking behavior. Secondly, we use the t-distributed Stochastic Neighbor Embedding(T-sne) algorithm to reduce the dimension of the data to visualize the clustering result. Finally, the learner's help-seeking behavior data and the help-seeking behavior classification labels are used as training data to train the naivebayesian model so as to automatically obtain the help-seeking behavior classification for the data generated by the new learner. Via the analysis and processing of the help-seeking behavior data using the method proposed in this paper, it shows that this method can effectively find online learners' help-seeking behavior classifications.
暂无评论