Web information extraction is viewed as a classification process and a competing classification method is presented to extract Web information directly through classification. Web fragments are represented with three ...Web information extraction is viewed as a classification process and a competing classification method is presented to extract Web information directly through classification. Web fragments are represented with three general features and the similarities between fragments are then defined on the bases of these features. Through competitions of fragments for different slots in information templates, the method classifies fragments into slot classes and filters out noise information. Far less annotated samples are needed as compared with rule-based methods and therefore it has a strong portability. Experiments show that the method has good performance and is superior to DOM-based method in information extraction. Key words information extraction - competing classification - feature extraction - wrapper induction CLC number TP 311 Foundation item: Supported by the National Natural Science Foundation of China (60303024)Biography: LI Xiang-yang (1974-), male, Ph. D. Candidate, research direction: information extraction, natural language processing.展开更多
To solve the problem of the inadequacy of semantic processing in the intelligent question answering system, an integrated semantic similarity model which calculates the semantic similarity using the geometric distance...To solve the problem of the inadequacy of semantic processing in the intelligent question answering system, an integrated semantic similarity model which calculates the semantic similarity using the geometric distance and information content is presented in this paper. With the help of interrelationship between concepts, the information content of concepts and the strength of the edges in the ontology network, we can calculate the semantic similarity between two concepts and provide information for the further calculation of the semantic similarity between user’s question and answers in knowledge base. The results of the experiments on the prototype have shown that the semantic problem in natural language processing can also be solved with the help of the knowledge and the abundant semantic information in ontology. More than 90% accuracy with less than 50 ms average searching time in the intelligent question answering prototype system based on ontology has been reached. The result is very satisfied. Key words intelligent question answering system - ontology - semantic similarity - geometric distance - information content CLC number TP39 Foundation item: Supported by the important science and technology item of China of “The 10th Five-year Plan” (2001BA101A05-04)Biography: LIU Ya-jun (1953-), female, Associate professor, research direction: software engineering, information processing, data-base application.展开更多
Clustering in high-dimensional space is an important domain in data mining. It is the process of discovering groups in a high-dimensional dataset, in such way, that the similarity between the elements of the same clus...Clustering in high-dimensional space is an important domain in data mining. It is the process of discovering groups in a high-dimensional dataset, in such way, that the similarity between the elements of the same cluster is maximum and between different clusters is minimal. Many clustering algorithms are not applicable to high-dimensional space for its sparseness and decline properties. Dimensionality reduction is an effective method to solve this problem. The paper proposes a novel clustering algorithm CFSBC based on closed frequent itemsets derived from association rule mining, which can get the clustering attributes with high efficiency. The algorithm has several advantages. First, it deals effectively with the problem of dimensionality reduction. Second, it is applicable to different kinds of attributes. Third, it is suitable for very large data sets. Experiment shows that the proposed algorithm is effective and efficient. Key words clustering - closed frequent itemsets - association rule - clustering attributes CLC number TP 311 Foundation item: Supported by the National Natural Science Foundation of China (70371015)Biography: NI Wei-wei (1979-), male, Ph. D candidate, research direction: data mining and knowledge discovery.展开更多
文摘Web information extraction is viewed as a classification process and a competing classification method is presented to extract Web information directly through classification. Web fragments are represented with three general features and the similarities between fragments are then defined on the bases of these features. Through competitions of fragments for different slots in information templates, the method classifies fragments into slot classes and filters out noise information. Far less annotated samples are needed as compared with rule-based methods and therefore it has a strong portability. Experiments show that the method has good performance and is superior to DOM-based method in information extraction. Key words information extraction - competing classification - feature extraction - wrapper induction CLC number TP 311 Foundation item: Supported by the National Natural Science Foundation of China (60303024)Biography: LI Xiang-yang (1974-), male, Ph. D. Candidate, research direction: information extraction, natural language processing.
文摘To solve the problem of the inadequacy of semantic processing in the intelligent question answering system, an integrated semantic similarity model which calculates the semantic similarity using the geometric distance and information content is presented in this paper. With the help of interrelationship between concepts, the information content of concepts and the strength of the edges in the ontology network, we can calculate the semantic similarity between two concepts and provide information for the further calculation of the semantic similarity between user’s question and answers in knowledge base. The results of the experiments on the prototype have shown that the semantic problem in natural language processing can also be solved with the help of the knowledge and the abundant semantic information in ontology. More than 90% accuracy with less than 50 ms average searching time in the intelligent question answering prototype system based on ontology has been reached. The result is very satisfied. Key words intelligent question answering system - ontology - semantic similarity - geometric distance - information content CLC number TP39 Foundation item: Supported by the important science and technology item of China of “The 10th Five-year Plan” (2001BA101A05-04)Biography: LIU Ya-jun (1953-), female, Associate professor, research direction: software engineering, information processing, data-base application.
文摘Clustering in high-dimensional space is an important domain in data mining. It is the process of discovering groups in a high-dimensional dataset, in such way, that the similarity between the elements of the same cluster is maximum and between different clusters is minimal. Many clustering algorithms are not applicable to high-dimensional space for its sparseness and decline properties. Dimensionality reduction is an effective method to solve this problem. The paper proposes a novel clustering algorithm CFSBC based on closed frequent itemsets derived from association rule mining, which can get the clustering attributes with high efficiency. The algorithm has several advantages. First, it deals effectively with the problem of dimensionality reduction. Second, it is applicable to different kinds of attributes. Third, it is suitable for very large data sets. Experiment shows that the proposed algorithm is effective and efficient. Key words clustering - closed frequent itemsets - association rule - clustering attributes CLC number TP 311 Foundation item: Supported by the National Natural Science Foundation of China (70371015)Biography: NI Wei-wei (1979-), male, Ph. D candidate, research direction: data mining and knowledge discovery.
