An Ensemble Semantic Textual Similarity Measure Based on Multiple Evidences for Biomedical Documents.

With the increasing volume of the published biomedical literature, the fast and effective retrieval of the literature on the sequence, structure, and function of biological entities is an essential task for the rapid development of biology and medicine. To capture the semantic information in biomedical literature more effectively when biomedical documents are clustered, we propose a new multi-evidence-based semantic text similarity calculation method. Two semantic similarities and one content similarity are used, in which two semantic similarities include MeSH-based semantic similarity and word embedding-based semantic similarity. To fuse three different similarities more effectively, after, respectively, calculating two semantic and one content similarities between biomedical documents, feedforward neural network is applied to integrate the two semantic similarities. Finally, weighted linear combination method is used to integrate the semantic and content similarities. To evaluate the effectiveness, the proposed method is compared with the existing basic methods, and the proposed method outperforms the existing related methods. Based on the proven results of this study, this method can be used not only in actual biological or medical experiments such as protein sequence or function analysis but also in biological and medical research fields, which will help to provide, use, and understand thematically consistent documents.