arXiv (Cornell University) · 2020 · 17 citations · 7 references
ComputingBiomedical InformaticsComputer ScienceComputational LinguisticsNatural Language ProcessingText AnalyticsAnnotationInformation ExtractionLiterature MiningNamed Entity RecognitionText MiningText SummarizationData AnnotationEntity Mention DetectionEvidence ExtractionInformation Extraction From TextNatural Language QueryingOpen Information ExtractionSummarization
We created this EVIDENCEMINER system for automatic textual evidence mining in COVID-19 literature. EVIDENCEMINER is a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. It is constructed in a completely automated way without any human effort for training data annotation. EVIDENCEMINER is supported by novel data-driven methods for distantly supervised named entity recognition and open information extraction. The named entities and meta-patterns are pre-computed and indexed offline to support fast online evidence retrieval. The annotation results are also highlighted in the original document for better visualization. EVIDENCEMINER also includes analytic functionalities such as the most frequent entity and relation summarization.
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Learning Named Entity Tagger using Domain-Specific Dictionary
Jingbo Shang, Liyuan Liu, Xiaotao Gu et al. · 2018 · 233 citations · Full text
Meng Jiang, Jingbo Shang, Taylor Cassidy et al. · 2017 · 74 citations
LitSense: making sense of biomedical literature at sentence level
Alexis Allot, Qingyu Chen, Sun Kim et al. · Nucleic Acids Research · 2019 · 62 citations · Full text
Distantly Supervised Biomedical Named Entity Recognition with Dictionary Expansion