Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel method of recognizing location names from Chinese texts based on max-margin Markov  N etwork (M 3 Net) 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 part-of-speech (POS) tag, the information whether a character appears in the location name characteristic word table and context information are extracted as the features. The F-measure is up to 90.57% based on 1-order M 3 Net which is better than that based on either SVM or CRFs in open test on MSRA dataset.
['Lishuang Li', 'Zhuoye Ding', 'Degen Huang']
I am writing a research paper on a idea Recognizing location names from Chinese texts based on Max-Margin Markov Network
[{"from": "human", "value": "I am writing a research paper on a idea Recognizing location names from Chinese texts based on Max-Margin Markov Network"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel method of recognizing location names from Chinese texts based on max-margin Markov  N etwork (M 3 Net) 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 part-of-speech (POS) tag, the information whether a character appears in the location name characteristic word table and context information are extracted as the features. The F-measure is up to 90.57% based on 1-order M 3 Net which is better than that based on either SVM or CRFs in open test on MSRA dataset."}]