Utilizing external corpora through kernel function: application in biomedical named entity recognition

Performance of word sequential labelling tasks like named entity recognition and parts-of-speech tagging largely depends on the features chosen in the task. But, in general representing a word as well as capturing its characteristics properly through a set of features is quite difficult. Moreover, external resources often become essential in order to build a high-performance system. But, acquiring required knowledge demands domain-specific processing and feature engineering. Kernel functions along with support vector machine may offer an alternative way to more efficiently capture similarity between words using both the local context and the external corpora. In this paper, we aim to compute similarity between the words using their context information, syntactic information and occurrence statistics in external corpora. This similarity value is gathered through a kernel function. The proposed kernel function combines two sub-kernels. One of these captures global information through words co-occurrence statistics accumulated from a large corpora. The second kernel captures local semantic information of the words through word specific parse tree fragmentation. We test this proposed kernel using JNLPBA 2004 Biomedical Named Entity Recognition and BioCreative II 2006 Gene Mention Recognition task data-sets. In our experiments, we observe that the proposed method is effective on both the data-sets.