earticle

영어

The traditional information extraction methods based on specific domain usually depend on the domain dictionaries to discover the text feature. It is inconvenient for reproducing and difficult to transplant in multi-domain environment. The application scope is limited seriously. Oriented to the deficiencies above, a multi-domain web text feature extraction model for e-Science is proposed (named e-FTM). This model adopts the Chinese split words technology without dictionary into the process of multi-domain text feature discovery and avoids the dependency of domain dictionaries effectively. With the help of classification of common and individual features, the model tracks the generation and the development trend of domain events dynamically, and forms a couple of local data centers eventually. Through cooperative scheduling the domain knowledge between different local data centers, the knowledge utilization efficiency of the domain information in the global scope is improved sharply. To validate the performance, the experiments on the multi-domain text feature extraction, topic features dynamical tracking and the domain knowledge cooperative scheduling demonstrate that the model has higher application validity and practicality in e-Science environment.