Chinese medicine （CM） is a discipline with its own distinct methodologies and philosophical principles. The main method of treatment in CM is to use herbal prescriptions. Typically, a number of herbs are combined to...Chinese medicine （CM） is a discipline with its own distinct methodologies and philosophical principles. The main method of treatment in CM is to use herbal prescriptions. Typically, a number of herbs are combined to form a formula and different formulae are prescribed for different patients. Regularities in the mixture of herbs in the prescriptions are important for both clinical treatment and novel patent medicine development. In this study, we analyze CM formula data using latent tree （LT） models. Interesting regularities are discovered. Those regularities are of interest to students of CM as well as pharmaceutical companies that manufacture medicine using Chinese herbs.展开更多
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 ambigu...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.展开更多
Our banner in the hotel lobby Report on 1st Asia-Pacific Summer School on Trusted Infrastructure Technolo- gies(APTISS'07)was held during the week of August 20-24,at the International Con- ference Center Hotel,Cit...Our banner in the hotel lobby Report on 1st Asia-Pacific Summer School on Trusted Infrastructure Technolo- gies(APTISS'07)was held during the week of August 20-24,at the International Con- ference Center Hotel,City of ZhuHai, GuangDong Province,China.展开更多
基金Supported by Program of Beijing Municipal S&T Commission, China(No.D08050703020803,D08050703020804)China NSFC Project(No.90709006)+1 种基金National Key Technology R&D Program k(2007BA110B06)China 973 Project(No.2011CB505101)
文摘Chinese medicine （CM） is a discipline with its own distinct methodologies and philosophical principles. The main method of treatment in CM is to use herbal prescriptions. Typically, a number of herbs are combined to form a formula and different formulae are prescribed for different patients. Regularities in the mixture of herbs in the prescriptions are important for both clinical treatment and novel patent medicine development. In this study, we analyze CM formula data using latent tree （LT） models. Interesting regularities are discovered. Those regularities are of interest to students of CM as well as pharmaceutical companies that manufacture medicine using Chinese herbs.
基金supported by the National Natural Science Foundation of China under Grant Nos.60833003 and 60773156
文摘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.
文摘Our banner in the hotel lobby Report on 1st Asia-Pacific Summer School on Trusted Infrastructure Technolo- gies(APTISS'07)was held during the week of August 20-24,at the International Con- ference Center Hotel,City of ZhuHai, GuangDong Province,China.
