Cai Peng, Ni Yuan, Zhu Huijia, Cao Feng
IBM China Research Lab, Shanghai, People's Republic of China.
Stud Health Technol Inform. 2012;180:599-603.
Clinical outcome information is helpful for clinicians to understand the effect of a given intervention. Generally speaking, the outcome generated by an intervention has several aspects, and each aspect has different polarity. In this work we adopt structure learning algorithm to extract fine-grained outcome information and then determine the polarity of each aspect by trained classifier. Word and POS features are integrated by structure learning algorithm. The performance is evaluated on our labeled dataset. Experimental results indicate that although POS information can improve the performance of fine-grained outcome extraction it should be leveraged carefully.
临床结果信息有助于临床医生了解特定干预措施的效果。一般来说，干预措施产生的结果有多个方面，且每个方面具有不同的极性。在这项工作中，我们采用结构学习算法来提取细粒度的结果信息，然后通过训练好的分类器确定每个方面的极性。结构学习算法将单词和词性特征进行整合。在我们的标记数据集上对性能进行评估。实验结果表明，尽管词性信息可以提高细粒度结果提取的性能，但应谨慎使用。
