Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Comparison is a popular way for people to discover the commonality and difference beten two entities (e.g. product, person, company, event, etc.). It would be very useful to automatically provide comparison results for the user. The prerequisite step of this task is to find comparable entities. ,  propose a novel b mining system to address the task of finding comparable entities for a given single entity. First, the system uses a bootstrapping method to find candidate entities for the given entity through natural language analysis in the snippets of search engine results. Then, the system uses set expansion techniques to find more candidate entities though semi-structured HTML analysis in the downloaded b pages. Finally, the system uses a supervised learning method to classify the candidate entities into either comparable or incomparable by incorporating linguistic, statistical and semantic features. Experimental results demonstrate that our proposed framework can outperform the baseline systems.
['Xiaojiang Huang', 'Xiaojun Wan', 'Jianguo Xiao']
I am writing a research paper on a idea Learning to find comparable entities on the web
[{"from": "human", "value": "I am writing a research paper on a idea Learning to find comparable entities on the web"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Comparison is a popular way for people to discover the commonality and difference beten two entities (e.g. product, person, company, event, etc.). It would be very useful to automatically provide comparison results for the user. The prerequisite step of this task is to find comparable entities. ,  propose a novel b mining system to address the task of finding comparable entities for a given single entity. First, the system uses a bootstrapping method to find candidate entities for the given entity through natural language analysis in the snippets of search engine results. Then, the system uses set expansion techniques to find more candidate entities though semi-structured HTML analysis in the downloaded b pages. Finally, the system uses a supervised learning method to classify the candidate entities into either comparable or incomparable by incorporating linguistic, statistical and semantic features. Experimental results demonstrate that our proposed framework can outperform the baseline systems."}]