Recognizing Small-Sample Biomedical Named Entity Based on Contextual Domain Relevance

Existing named entity recognition methods are often based on large training samples and cannot effectively recognize fine-grained domain entities with small sample sizes In order to solve this problem, this paper proposes an unsupervised method based on contextual domain relevance for recognizing biomedical named entities with small sample sizes. Based on the distributed semantic model, the statistical and linguistic features of candidate entities in corpora are described by using occurrence frequencies of contexts of candidate entities. Furthermore, the entity-corpus relevance assumption, the log-likelihood ratio and the domain-dependent function are adopted for recognizing objective entities. Experimental results show that, the proposed method can effectively reduce manual interventions and improve the precision rate and recall rate of small-sample biomedical named entity recognition.