Biomedical Mention Disambiguation using a Deep Learning Approach
This addresses the issue of imprecise disambiguation in biomedical text mining, which is incremental as it improves upon existing rule-based methods.
The paper tackled the problem of ambiguous biomedical named entity mentions by developing a deep learning-based disambiguation method, which achieved F1-scores of 91.94% (micro-averaged) and 85.42% (macro-averaged), a substantial improvement over a rule-based approach with scores of 71.29% and 41.19%.
Automatically locating named entities in natural language text - named entity recognition - is an important task in the biomedical domain. Many named entity mentions are ambiguous between several bioconcept types, however, causing text spans to be annotated as more than one type when simultaneously recognizing multiple entity types. The straightforward solution is a rule-based approach applying a priority order based on the precision of each entity tagger (from highest to lowest). While this method is straightforward and useful, imprecise disambiguation remains a significant source of error. We address this issue by generating a partially labeled corpus of ambiguous concept mentions. We first collect named entity mentions from multiple human-curated databases (e.g. CTDbase, gene2pubmed), then correlate them with the text mined span from PubTator to provide the context where the mention appears. Our corpus contains more than 3 million concept mentions that ambiguous between one or more concept types in PubTator (about 3% of all mentions). We approached this task as a classification problem and developed a deep learning-based method which uses the semantics of the span being classified and the surrounding words to identify the most likely bioconcept type. More specifically, we develop a convolutional neural network (CNN) and along short-term memory (LSTM) network to respectively handle the semantic syntax features, then concatenate these within a fully connected layer for final classification. The priority ordering rule-based approach demonstrated F1-scores of 71.29% (micro-averaged) and 41.19% (macro-averaged), while the new disambiguation method demonstrated F1-scores of 91.94% (micro-averaged) and 85.42% (macro-averaged), a very substantial increase.