Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In many important text classification problems, acquiring class labels for training documents is costly, while gathering large quantities of unlabeled data is cheap.  shows that the accuracy of text classifiers trained with a small number of labeled documents can be improved by augmenting this small training set with a large pool of unlabeled documents.  present a theoretical argument showing that, under common assumptions, unlabeled data contain information about the target function.  then introduce an algorithm for learning from labeled and unlabeled text based on the combination of Expectation-Maximization with a naive Bayes classifier. The algorithm first trains a classifier using the available labeled documents, and probabilistically labels the unlabeled documents; it then trains a new classifier using the labels for all the documents, and iterates to convergence. Experimental results, obtained using text from three different realworld tasks, show that the use of unlabeled data reduces classification error by up to 33%.
['Kamal Nigam', 'Andrew McCallum', 'Sebastian Thrun', 'Tom M. Mitchell']
I am writing a research paper on a idea Learning to classify text from labeled and unlabeled documents
[{"from": "human", "value": "I am writing a research paper on a idea Learning to classify text from labeled and unlabeled documents"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In many important text classification problems, acquiring class labels for training documents is costly, while gathering large quantities of unlabeled data is cheap.  shows that the accuracy of text classifiers trained with a small number of labeled documents can be improved by augmenting this small training set with a large pool of unlabeled documents.  present a theoretical argument showing that, under common assumptions, unlabeled data contain information about the target function.  then introduce an algorithm for learning from labeled and unlabeled text based on the combination of Expectation-Maximization with a naive Bayes classifier. The algorithm first trains a classifier using the available labeled documents, and probabilistically labels the unlabeled documents; it then trains a new classifier using the labels for all the documents, and iterates to convergence. Experimental results, obtained using text from three different realworld tasks, show that the use of unlabeled data reduces classification error by up to 33%."}]