Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Existing template-independent b data extraction approaches adopt highly ineffective decoupled strategies---attempting to do data record detection and attribute labeling in two separate phases. ,  propose an integrated b data extraction paradigm with hierarchical models. The proposed model is called Dynamic Hierarchical Markov Random Fields (DHMRFs). DHMRFs take structural uncertainty into consideration and define a joint distribution of both model structure and class labels. The joint distribution is an exponential family distribution. As a conditional model, DHMRFs relax the independence assumption as made in directed models. Since exact inference is intractable, a variational method is developed to learn the model's parameters and to find the MAP model structure and label assignments.  apply DHMRFs to a real-world b data extraction task. Experimental results show that: (1) integrated b data extraction models can achieve significant improvements on both record detection and attribute labeling compared to decoupled models; (2) in diverse b data extraction DHMRFs can potentially address the blocky artifact issue which is suffered by fixed-structured hierarchical models.
['Jun Zhu', 'Zaiqing Nie', 'Bo Zhang', 'Ji-Rong Wen']
I am writing a research paper on a idea Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction
[{"from": "human", "value": "I am writing a research paper on a idea Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Existing template-independent b data extraction approaches adopt highly ineffective decoupled strategies---attempting to do data record detection and attribute labeling in two separate phases. ,  propose an integrated b data extraction paradigm with hierarchical models. The proposed model is called Dynamic Hierarchical Markov Random Fields (DHMRFs). DHMRFs take structural uncertainty into consideration and define a joint distribution of both model structure and class labels. The joint distribution is an exponential family distribution. As a conditional model, DHMRFs relax the independence assumption as made in directed models. Since exact inference is intractable, a variational method is developed to learn the model's parameters and to find the MAP model structure and label assignments.  apply DHMRFs to a real-world b data extraction task. Experimental results show that: (1) integrated b data extraction models can achieve significant improvements on both record detection and attribute labeling compared to decoupled models; (2) in diverse b data extraction DHMRFs can potentially address the blocky artifact issue which is suffered by fixed-structured hierarchical models."}]