ArticleBMC bioinformatics2020
Exploiting sequence labeling framework to extract document-level relations from biomedical texts.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 20 citations in OpenAlex.
- Exploiting question-answer framework with multi-GRU to detect adverse drug reaction on social media.Scientific reports · 2025Article
- Assigning species information to corresponding genes by a sequence labeling framework.Database : the journal of biological databases and curation · 2022Article
- A sequence labeling framework for extracting drug-protein relations from biomedical literature.Database : the journal of biological databases and curation · 2022Article
- Biomedical relation extraction via knowledge-enhanced reading comprehension.BMC bioinformatics · 2022Article
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundBoth intra- and inter-sentential semantic relations in biomedical texts provide valuable information for biomedical research. However, most existing methods either focus on extracting intra-sentential relations and ignore inter-sentential ones or fail to extract inter-sentential relations accurately and regard the instances containing entity relations as being independent, which neglects the interactions between relations. We propose a novel sequence labeling-based biomedical relation extraction method named Bio-Seq. In the method, sequence labeling framework is extended by multiple specified feature extractors so as to facilitate the feature extractions at different levels, especially at the inter-sentential level. Besides, the sequence labeling framework enables Bio-Seq to take advantage of the interactions between relations, and thus, further improves the precision of document-level relation extraction.
resultsOur proposed method obtained an F1-score of 63.5% on BioCreative V chemical disease relation corpus, and an F1-score of 54.4% on inter-sentential relations, which was 10.5% better than the document-level classification baseline. Also, our method achieved an F1-score of 85.1% on n2c2-ADE sub-dataset.
conclusionSequence labeling method can be successfully used to extract document-level relations, especially for boosting the performance on inter-sentential relation extraction. Our work can facilitate the research on document-level biomedical text mining.
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