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It is vital to develop automatic information extraction systems to help researchers cope up with the vast amount of data available on the Internet. In this paper, we describe a framework to extract precise information about coexpression relationship among genes, from published literature using a supervised machine learning approach. We use a graphical model, Dynamic Conditional Random Fields (DCRFs),...
Text categorization (TC) is an important component in many information organization and information management tasks. Two key issues in TC are feature coding and classifier design. The Euclidean distance is usually chosen as the similarity measure in K-nearest neighbor classification algorithm. All the features of each vector have different functions in describing samples. So we can decide different...
Traditional text classifiers usually concern nothing about the producing time of samples, but many samples may involve seasonal features, which contain necessary prior information for classification. This paper firstly discovered the emporal relation between classes by means of chi-square test on a 2 * p dimensional contingency table for the data set of the mayor's complain telephone texts, and then...
Recognizing and extracting exact name entities, like Persons, Locations, Organizations, Dates and Times are very useful to mining information from electronics resources and text. Learning to extract these types of data is called Named Entity Recognition (NER) task. Proper named entity recognition and extraction is important to solve most problems in hot research area such as Question Answering and...
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