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This paper presents a novel method of recognizing location names from Chinese texts based on max-margin Markov Network (M3Net) owing to its ability to exploit very high dimensional feature spaces (using the kernel trick) while at the same time dealing with structured data compared with Support Vector Machine (SVM) and conditional random fields (CRFs). In our model, the character itself, character-based...
This paper presents a hybrid model and the corresponding algorithm combining conditional random fields (CRFs) with statistical methods to improve the performance of CRFs for the task of Chinese named entity recognition (NER). CRFs has a good performance in the task of sequence labeling. In the experiment of recognizing Chinese named entity with CRFs, it can be found that the wrong tags labeled by...
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