当前对新词发现、情感词极性标注与情感词库构建的研究比较多,却少有一个专门针对新情感词识别的方法。提出一种基于OC-SVM的新情感词识别方法,通过种子词扩展方法获得词语集,并用旧词典、词频和停用词等对扩展的词进行过滤,获取新词,对新词获取的实验评估显示在适当的F值下,正确率可以达到45.5%。由于情感词和非情感词训练集的不平衡性,采用词频、相邻词及其词性等作为特征用OC-SVM(one-class support vector machine)对新词进行分类,获得新情感词,构建一个有效的新情感词识别系统。实验结果在召回率为26.6%的情况下,正确率可以达到45.7%,证明了算法的有效性。
In this paper we propose a multiple feature approach for the normalization task which can map each disorder mention in the text to a unique unified medical language system（UMLS）concept unique identifier（CUI）. We develop a two-step method to acquire a list of candidate CUIs and their associated preferred names using UMLS API and to choose the closest CUI by calculating the similarity between the input disorder mention and each candidate. The similarity calculation step is formulated as a classification problem and multiple features（string features,ranking features,similarity features,and contextual features） are used to normalize the disorder mentions. The results show that the multiple feature approach improves the accuracy of the normalization task from 32.99% to 67.08% compared with the Meta Map baseline.
