关键词: 命名实体识别, 互联网资源, 迭代框架, 平均感知器

Abstract: The first step of medical information extraction is named entity recognition, but the lack of open medical corpus makes it rather difficult. Existing work commonly relies on a small amount of manually annotated texts, so that it can??t be widely promoted. As a collection of large amounts of data, the Internet can be used to extract medical knowledge. Considering the size and characteristic of Internet, this paper proposes an iterative framework to exploit it. In order to deal with the effect drop of domain differences, a method of fusing universal model and domain dictionary is used to annotate the text. To avoid retraining the model, an online method is used to build the model. This paper integrates multiple features in the model, including lexical features, affixes features, word length features and so on. Besides, this paper gives a heuristic model compression method to enhance the usability of the model. The  experimental results show that the proposed strategies are effective.

Key words: named entity recognition, Internet resources, iterative framework, average perceptron