Knowledge-Base Enriched Word Embeddings for Biomedical Domain
This addresses the challenge of accurate word representations in biomedical text, where domain-specific terms are common, but it is incremental as it builds on existing embedding techniques.
The authors tackled the problem of poor word embeddings for domain-specific terms with insufficient local context by proposing a model that jointly leverages corpora and domain knowledge-bases, achieving validated effectiveness on biomedical concept similarity and relatedness tasks.
Word embeddings have been shown adept at capturing the semantic and syntactic regularities of the natural language text, as a result of which these representations have found their utility in a wide variety of downstream content analysis tasks. Commonly, these word embedding techniques derive the distributed representation of words based on the local context information. However, such approaches ignore the rich amount of explicit information present in knowledge-bases. This is problematic, as it might lead to poor representation for words with insufficient local context such as domain specific words. Furthermore, the problem becomes pronounced in domain such as bio-medicine where the presence of these domain specific words are relatively high. Towards this end, in this project, we propose a new word embedding based model for biomedical domain that jointly leverages the information from available corpora and domain knowledge in order to generate knowledge-base powered embeddings. Unlike existing approaches, the proposed methodology is simple but adept at capturing the precise knowledge available in domain resources in an accurate way. Experimental results on biomedical concept similarity and relatedness task validates the effectiveness of the proposed approach.