BioSentVec: creating sentence embeddings for biomedical texts
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- Type
- article
- Published
- 2018-10-22
- Cited by
- 240
- References
- 23
- Access
- Open access
- OpenAlex
- https://openalex.org/W2896016608
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:53047553
Keywords
Sentence, Complement (music), Set (abstract data type), Word (group theory), Similarity (geometry)
References
- A Review on Multi-Label Learning Algorithms
- Hallmarks of cancer: the next generation.
- Distributed Representations of Sentences and Documents
- Representation Learning: A Review and New Perspectives
- Automatic semantic classification of scientific literature according to the hallmarks of cancer
- MIMIC-III, a freely accessible critical care database
- How to Train good Word Embeddings for Biomedical NLP
- A passage retrieval method based on probabilistic information retrieval model and UMLS concepts in biomedical question answering
- Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features
- Opportunities and obstacles for deep learning in biology and medicine
- BIOSSES: a semantic sentence similarity estimation system for the biomedical domain
- A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis
- bigNN: An open-source big data toolkit focused on biomedical sentence classification
- A Comparison of Word Embeddings for the Biomedical Natural Language Processing
- Universal Sentence Encoder
- Sentence Similarity Measures Revisited: Ranking Sentences in PubMed Documents
- MedSTS: a resource for clinical semantic textual similarity
- LitSense: making sense of biomedical literature at sentence level
- Unsupervised Low-Dimensional Vector Representations for Words, Phrases and Text that are Transparent, Scalable, and produce Similarity Metrics that are not Redundant with Neural Embeddings
- Deep learning for extracting protein-protein interactions from biomedical literature
Cited by
- ML-Net: multi-label classification of biomedical texts with deep neural networks
- Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning From Radiology Reports and Label Ontology
- LitSense: making sense of biomedical literature at sentence level
- A self-attention based deep learning method for lesion attribute detection from CT reports
- RedMed: Extending drug lexicons for social media applications
- Robust Representation Learning of Biomedical Names
- Medical Word Embeddings for Spanish: Development and Evaluation
- Personalized Feed/Query-formulation, Predictive Impact, and Ranking
- VIST - a Variant-Information Search Tool for precision oncology
- Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment
- Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering
- Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets
- Evaluation of Five Sentence Similarity Models on Electronic Medical Records
- Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification
- Using Clinical Notes with Time Series Data for ICU Management
- Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings
- Extracting and Representing Entities, Types, and Relations
- Extraction of chemical–protein interactions from the literature using neural networks and narrow instance representation
- Active Learning with Siamese Twins for Sequence Tagging
- Representing document-level semantics of biomedical literature using pre-trained embedding models: Novel assessments
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