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Ambiguous words refer to words that have multiple meanings such as apple, window. In text classification they are usually removed by feature reduction methods like Information Gain. Sometimes there are too many ambiguous words in the corpus, which makes throwing away all of them not a viable option, as in the case when classifying documents from the Web. In this paper we look for a method to classify Titled documents with the help of ambiguous words. Titled documents are a kind of documents that have a simple structure containing a title and an excerpt. News, messages, and paper abstracts with titles are examples of titled documents. Instead of introducing another feature reduction method, we describe a framework to make the best use of ambiguous words in the titled documents. The framework improves the performance of a traditional bag-of-words classifier with the help of a bag-of-word-pairs classifier. The framework is implemented using one of the most popular classifiers, Multinomial NaiveBayes (MNB) as an example. The experiments with three real life datasets show that in our framework the MNB model performs much better than traditional MNB classifier and a naive weighted algorithm, which simply puts more weight on words in the title.