Improving Information Extraction using Knowledge Model

This paper describes the work on automated Information Extraction that accepts arbitrary text and extracts information from the text. A new approach to implement Information Extraction system is proposed in this paper. Firstly, the article will be decomposed according to paragraph, sentence and phrase. Every sentence will be compared with the knowledge node, and then append the information extracted to the knowledge model. Finally, the answers are generated to the questions about the input text. With the experimental corpus the accuracy rate of knowledge matching is 63.5%, and accuracy rate of question answering is 65.0% with the system knowledge model.