%0 Journal Article
%T Loose coupling algorithm for biomedical named entity recognition<br>一种松耦合的生物医学命名实体识别算法
%A HU Jun-feng
%A CHEN Rong
%A CHEN Yuan
%A CHEN Hao
%A YU Zhong-hua
%A <br>胡俊锋
%A 陈蓉
%A 陈源
%A 陈浩
%A 于中华
%J 计算机应用
%D 2007
%I 
%X The rapid development of biology and medicine in recent years leads to speedy accumulation of gigabyte biomedical information. How to use technical methods to mine and utilize the information becomes more and more important. Biomedical Named Entity Recognition (Bio-NER) is a basal work for mining and utilizing biomedical literatures. Concerning the difficulties and problems of the existing Bio-NER algorithms, a loose coupling algorithm named LCA for Bio-NER was proposed. The biomedical named entities were recognized based on heuristic rule filter, POS pattern matching pattern matching and modified Hidden Markov Model (HMM) approaches. The experimental results on GENIA corpus 3.02 show that the precision and recall of LCA are around 80% and 89% respectively, higher than the results of the related works.
%K biomedical named entity
%K heuristic rule filter
%K Parts Of Speech (POS) Pattern Matching
%K etyma matching
%K Hidden Markov Model (HMM)
%K loose coupling algorithm<br>生物医学命名实体
%K 启发规则过滤器
%K 词性模板匹配
%K 词根匹配
%K 隐马尔科夫模型
%K 松耦合算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=628CAF6EE781FBFB16FDD59CE551A9A0&yid=A732AF04DDA03BB3&vid=DB817633AA4F79B9&iid=708DD6B15D2464E8&sid=17FC9F71D1283CBD&eid=2C9C419B408CCD32&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=18