Exploiting Tree Kernels for high performance Chemical Induced Disease relation extraction

Abstract

Machine learning approaches based on supervised classification have emerged as effective methods for Biomedical relation extraction such as the Chemical-Induced- Disease (CID) task. These approaches owe their success to a rich set of features crafted from the lexical and syntactic regularities in the text. Kernel methods are an effective alternative to manual feature engineering and have been successfully used in similar tasks such as text classification. In this paper, we study the effectiveness of tree kernels for Chemical-Disease relation extraction. Our experiments demonstrate that subset tree kernels increase the F-score to 61:7% as compared to 57:9% achieved with simple feature enginee..