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A novel deep learning method for extracting unspecific biomedical relation
Author(s) -
Bai Tian,
Wang Chunyu,
Wang Ye,
Huang Lan,
Xing Fuyong
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5005
Subject(s) - relation (database) , computer science , benchmark (surveying) , artificial intelligence , relationship extraction , biomedical text mining , focus (optics) , natural language processing , deep learning , machine learning , data mining , text mining , physics , geodesy , optics , geography
Summary Biomedical relation extraction is an important research subject in Natural language processing (NLP). Deep learning technology has shown greater value in improving accuracy of relation extraction results recently. Existing methods mostly focus on extracting (1) specific relation from short texts (eg, drug‐drug interaction and protein‐protein interaction) and (2) unspecific relation from full text corpora. However, extracting unspecific relation from short text, which is more and more important in practical use, is rarely studied. In this paper, a new model called MAT‐LSTM is proposed to extract unspecific relation from short text in biomedical literatures. Experiments on two Biocreative benchmark datasets and one BioNLP benchmark datasets were made to measure the validity of the proposed model MAT‐LSTM, and better performance is achieved. The MAT‐LSTM model is also applied practically in extracting unspecific relation contained in the PubMed literatures. The results extracted from PubMed by using the proposed model were verified by experts mostly, indicating the practical value of the MAT‐LSTM model.