The classification of textual data often yields important information. Most
classifiers work in a closed world setting where the classifier is trained on a
known corpus, and then it is tested on unseen examples that belong to one of
the classes seen during training. Despite the usefulness of this design, often
there is a need to classify unseen examples that
研究中提出了一种 Open set recognition based on Pseudo unseen data Generation (OPG) 方法，利用相似度学习，通过先学习一个 closed set classifier，再学习如何将已知类别分别与伪未知类别进行比较（通过数据分布转化生成的），从而检测未知类别（或未看到过的类别），实验结果表明，这种基于相似度特征的方法可成功区分开放性识别基准数据集中已知和未知的类别。