Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Contextual Advertising (CA) refers to the placement of ads that are contextually related to the b page content. The science of CA deals with the task of finding advertising keywords from b pages.  present a different candidate selection method to extract advertising keywords from a b page. This method makes use of Part-of-Speech (POS) patterns that restrict the number of potential candidates a classifier has to handle. It fetches words/phrases that belong to the selected set of POS patterns.  design four systems based on chunking method and the features they use. These systems are trained on a naive Bayes classifier with a set of b pages annotated with 'advertising' keywords. The systems can then find advertising keywords from previously unseen b pages. Empirical evaluation shows that systems using the proposed chunking method perform better than the systems using N-Gram based chunking. All improvements in the systems are found statistically significant at a 99% confidence interval.
['Kushal S. Dave', 'Vasudeva Varma']
I am writing a research paper on a idea Pattern based keyword extraction for contextual advertising
[{"from": "human", "value": "I am writing a research paper on a idea Pattern based keyword extraction for contextual advertising"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Contextual Advertising (CA) refers to the placement of ads that are contextually related to the b page content. The science of CA deals with the task of finding advertising keywords from b pages.  present a different candidate selection method to extract advertising keywords from a b page. This method makes use of Part-of-Speech (POS) patterns that restrict the number of potential candidates a classifier has to handle. It fetches words/phrases that belong to the selected set of POS patterns.  design four systems based on chunking method and the features they use. These systems are trained on a naive Bayes classifier with a set of b pages annotated with 'advertising' keywords. The systems can then find advertising keywords from previously unseen b pages. Empirical evaluation shows that systems using the proposed chunking method perform better than the systems using N-Gram based chunking. All improvements in the systems are found statistically significant at a 99% confidence interval."}]