{"list":"other","total":58,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1705.07874","title":"A Unified Approach to Interpreting Model Predictions","created":"2017-05-22","cited_by_count":6555},{"id":"1602.04938","title":"\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier","created":"2016-02-16","cited_by_count":5070},{"id":"1804.07461","title":"GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding","created":"2018-04-20","cited_by_count":4899},{"id":"1902.00751","title":"Parameter-Efficient Transfer Learning for NLP","created":"2019-02-02","cited_by_count":4236},{"id":"2108.07258","title":"On the Opportunities and Risks of Foundation Models","created":"2021-08-16","cited_by_count":4033},{"id":"1911.02116","title":"Unsupervised Cross-lingual Representation Learning at Scale","created":"2019-11-05","cited_by_count":3935},{"id":"1310.4546","title":"Distributed Representations of Words and Phrases and their Compositionality","created":"2013-10-16","cited_by_count":3474},{"id":"1802.05365","title":"Deep contextualized word representations","created":"2018-02-15","cited_by_count":2290},{"id":"1901.08746","title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","created":"2019-01-25","cited_by_count":1947},{"id":"2004.10964","title":"Don't Stop Pretraining: Adapt Language Models to Domains and Tasks","created":"2020-04-23","cited_by_count":1565},{"id":"1903.10676","title":"SciBERT: A Pretrained Language Model for Scientific Text","created":"2019-03-26","cited_by_count":1414},{"id":"1904.03323","title":"Publicly Available Clinical BERT Embeddings","created":"2019-04-06","cited_by_count":1002},{"id":"1902.10186","title":"Attention is not Explanation","created":"2019-02-26","cited_by_count":929},{"id":"2002.12327","title":"A Primer in BERTology: What we know about how BERT works","created":"2020-02-27","cited_by_count":815},{"id":"2005.00247","title":"AdapterFusion: Non-Destructive Task Composition for Transfer Learning","created":"2020-05-01","cited_by_count":788},{"id":"1908.04626","title":"Attention is not not Explanation","created":"2019-08-13","cited_by_count":636},{"id":"2003.02320","title":"Knowledge Graphs","created":"2020-03-04","cited_by_count":621},{"id":"1904.05342","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","created":"2019-04-10","cited_by_count":493},{"id":"1911.02508","title":"Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods","created":"2019-11-06","cited_by_count":443},{"id":"1803.07640","title":"AllenNLP: A Deep Semantic Natural Language Processing Platform","created":"2018-03-20","cited_by_count":347},{"id":"1906.05474","title":"Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets","created":"2019-06-13","cited_by_count":299},{"id":"1812.09449","title":"A Survey on Deep Learning for Named Entity Recognition","created":"2018-12-22","cited_by_count":282},{"id":"2010.11784","title":"Self-Alignment Pretraining for Biomedical Entity Representations","created":"2020-10-22","cited_by_count":237},{"id":"2004.10706","title":"CORD-19: The COVID-19 Open Research Dataset","created":"2020-04-22","cited_by_count":180},{"id":"2108.04840","title":"Post-hoc Interpretability for Neural NLP: A Survey","created":"2021-08-10","cited_by_count":156},{"id":"1710.06071","title":"PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts","created":"2017-10-17","cited_by_count":124},{"id":"1905.07213","title":"Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language","created":"2019-05-17","cited_by_count":109},{"id":"2006.01272","title":"Shapley explainability on the data manifold","created":"2020-06-01","cited_by_count":88},{"id":"2010.10392","title":"CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters","created":"2020-10-20","cited_by_count":87},{"id":"2010.01165","title":"Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit","created":"2020-10-02","cited_by_count":66},{"id":"2011.02947","title":"CODER: Knowledge infused cross-lingual medical term embedding for term normalization","created":"2020-11-05","cited_by_count":55},{"id":"2010.10391","title":"UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus","created":"2020-10-20","cited_by_count":53},{"id":"2008.13546","title":"Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs","created":"2020-08-04","cited_by_count":48},{"id":"2102.10062","title":"A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective","created":"2021-02-19","cited_by_count":38},{"id":"1906.05221","title":"A Model to Search for Synthesizable Molecules","created":"2019-06-12","cited_by_count":37},{"id":"1911.06446","title":"CASTER: Predicting Drug Interactions with Chemical Substructure Representation","created":"2019-11-15","cited_by_count":24},{"id":"1701.06279","title":"dna2vec: Consistent vector representations of variable-length k-mers","created":"2017-01-23","cited_by_count":16},{"id":"2204.09220","title":"LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs","created":"2022-04-20","cited_by_count":15},{"id":"2109.04810","title":"Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERT","created":"2021-09-10","cited_by_count":14},{"id":"1910.01116","title":"Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph","created":"2019-10-02","cited_by_count":12},{"id":"2003.12218","title":"Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision","created":"2020-03-27","cited_by_count":12},{"id":"2004.03659","title":"The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews","created":"2020-04-07","cited_by_count":11},{"id":"2006.15830","title":"Answering Questions on COVID-19 in Real-Time","created":"2020-06-29","cited_by_count":11},{"id":"1910.08534","title":"Many Faces of Feature Importance: Comparing Built-in and Post-hoc Feature Importance in Text Classification","created":"2019-10-18","cited_by_count":10},{"id":"1910.11241","title":"Healthcare NER Models Using Language Model Pretraining","created":"2019-10-23","cited_by_count":8},{"id":"2012.10928","title":"Explaining Black-box Models for Biomedical Text Classification","created":"2020-12-20","cited_by_count":7},{"id":"2005.12522","title":"What Are People Asking About COVID-19? A Question Classification Dataset","created":"2020-05-26","cited_by_count":5},{"id":"2005.06634","title":"Detecting Adverse Drug Reactions from Twitter through Domain-Specific Preprocessing and BERT Ensembling","created":"2020-05-11","cited_by_count":3},{"id":"2007.15681","title":"COVID-19 therapy target discovery with context-aware literature mining","created":"2020-07-30","cited_by_count":3},{"id":"2007.02452","title":"Exploratory Analysis of COVID-19 Related Tweets in North America to Inform Public Health Institutes","created":"2020-07-05","cited_by_count":2},{"id":"2101.07824","title":"Machine learning applications for COVID-19: A state-of-the-art review","created":"2021-01-19","cited_by_count":2},{"id":"2102.13139","title":"PharmKE: Knowledge Extraction Platform for Pharmaceutical Texts using Transfer Learning","created":"2021-02-25","cited_by_count":2},{"id":"2104.00975","title":"Use of 'off-the-shelf' information extraction algorithms in clinical informatics: a feasibility study of MetaMap annotation of Italian medical notes","created":"2021-04-02","cited_by_count":2},{"id":"2111.00611","title":"R-BERT-CNN: Drug-target interactions extraction from biomedical literature","created":"2021-10-31","cited_by_count":2}],"next_cursor":null}