A Machine Learning Model for Named Entity Recognition

In this paper, a novel unified named entity recognition model was proposed for extracting recruitment information in Web pages. The model provides a simple statistical framework to incorporate a wide variety of linguistic knowledge and statistical models in a unified way. In our approach, firstly, in order to emphasize the specific semantics and term space in the named entity, Multi-Rules are built for a better representation of the named entity. Secondly, an optimal algorithm of the hierarchically structured DSTCRFs is performed to pick out the structure attributes of the named entity from the recruitment knowledge and optimize the efficiency of the training. The experimental results showed that the accuracy rate has been significantly improved and the complexity of sample training has been decreased.