Phenotype Extraction Based on Word Embedding to Sentence Embedding Cascaded Approach.

As a significant determinant in the development of named entity recognition, phenotypic descriptions are normally presented differently in biomedical literature with the use of complicated semantics. In this paper, a novel approach has been proposed to identify plant phenotypes by adopting word embedding to sentence embedding cascaded approach. We make use of a word embedding method to find high-frequency phenotypes with original sentences used as input in a sentence embedding method. In doing so, a variety of complicated phenotypic expressions can be recognized accurately. Besides, the state-of-the-art word representation models have been compared and among them, skip-gram with negative sampling was selected with the best performance. To evaluate the performance of our approach, we applied it to the dataset composed of 56 748 PubMed abstracts of model organism Arabidopsis thaliana. The experiment results showed that our approach yielded the best performance, as it achieved a 2.588-fold increase in terms of the number of new phenotypic descriptions when compared to the original phenotype ontology.