Named entity recognition is a fundamental task in biomedical data mining. In this letter, a named entity recognition system based on CRFs （Conditional Random Fields） for biomedical texts is presented. The system makes extensive use of a diverse set of features, including local features, full text features and external resource features. All features incorporated in this system are described in detail, and the impacts of different feature sets on the performance of the system are evaluated. In order to improve the performance of system, post-processing modules are exploited to deal with the abbreviation phenomena, cascaded named entity and boundary errors identification. Evaluation on this system proved that the feature selection has important impact on the system performance, and the post-processing explored has an important contribution on system performance to achieve better resuits.
Popularity of blogs and the amount of information in the blogosphere increase so fast that it is difficult for Internet users to search the information they care about. Compared with conventional webs,links in the blogosphere are more abundant and conversations between bloggers are more fre-quent. This paper proposes a method of ranking bloggers based on link analysis,which can exemplify the characteristics of blogs,and reduce the influence of link spamming. This method can also bring convenience to users to read blogs,and it can supply a new methodology for information retrieval in the blogosphere. To ensure the reliability of the ranking results,some evaluation indicators of the im-portant bloggers are proposed,and the grading results of bloggers using the proposed method is compared with that using other indicators. At last,correlation analysis is used to verify the consistency between the proposed method and the evaluation indicators.
Yang Yuhang Yu Hao Zhao Tiejun Tan Hongye Zheng Dequan
