Automatic text summarization involves reducing a text document or a larger corpus of multiple documents to a short set of sentences or paragraphs that convey the main meaning of the text. In this paper, we discuss abo...Automatic text summarization involves reducing a text document or a larger corpus of multiple documents to a short set of sentences or paragraphs that convey the main meaning of the text. In this paper, we discuss about multi-document summarization that differs from the single one in which the issues of compression, speed, redundancy and passage selection are critical in the formation of useful summaries. Since the number and variety of online medical news make them difficult for experts in the medical field to read all of the medical news, an automatic multi-document summarization can be useful for easy study of information on the web. Hence we propose a new approach based on machine learning meta-learner algorithm called AdaBoost that is used for summarization. We treat a document as a set of sentences, and the learning algorithm must learn to classify as positive or negative examples of sentences based on the score of the sentences. For this learning task, we apply AdaBoost meta-learning algorithm where a C4.5 decision tree has been chosen as the base learner. In our experiment, we use 450 pieces of news that are downloaded from different medical websites. Then we compare our results with some existing approaches.展开更多
This work presents the design of an Expert System that aims to advice the club teams to buy a football player in the post that they needed. Suggesting different player in many posts by an expert person is based on foo...This work presents the design of an Expert System that aims to advice the club teams to buy a football player in the post that they needed. Suggesting different player in many posts by an expert person is based on football experience, knowledge about the player and the club that he works. For mechanization the ability of this person, we use Expert System because it can model the ability of a person in solving a problem. Visual Prolog language is used as a tool for designing our Expert System.展开更多
文摘Automatic text summarization involves reducing a text document or a larger corpus of multiple documents to a short set of sentences or paragraphs that convey the main meaning of the text. In this paper, we discuss about multi-document summarization that differs from the single one in which the issues of compression, speed, redundancy and passage selection are critical in the formation of useful summaries. Since the number and variety of online medical news make them difficult for experts in the medical field to read all of the medical news, an automatic multi-document summarization can be useful for easy study of information on the web. Hence we propose a new approach based on machine learning meta-learner algorithm called AdaBoost that is used for summarization. We treat a document as a set of sentences, and the learning algorithm must learn to classify as positive or negative examples of sentences based on the score of the sentences. For this learning task, we apply AdaBoost meta-learning algorithm where a C4.5 decision tree has been chosen as the base learner. In our experiment, we use 450 pieces of news that are downloaded from different medical websites. Then we compare our results with some existing approaches.
文摘This work presents the design of an Expert System that aims to advice the club teams to buy a football player in the post that they needed. Suggesting different player in many posts by an expert person is based on football experience, knowledge about the player and the club that he works. For mechanization the ability of this person, we use Expert System because it can model the ability of a person in solving a problem. Visual Prolog language is used as a tool for designing our Expert System.
