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Named entity recognition (NER) is a widely applicable natural language processing task and building block of question answering, topic modeling, information retrieval, etc.
Ranked #1 on Named Entity Recognition on LINNAEUS
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
Ranked #1 on Named Entity Recognition on NCBI-disease
Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive.
Ranked #1 on Relation Extraction on JNLPBA
CITATION INTENT CLASSIFICATION CLASSIFICATION DEPENDENCY PARSING LANGUAGE MODELLING MEDICAL NAMED ENTITY RECOGNITION PARTICIPANT INTERVENTION COMPARISON OUTCOME EXTRACTION RELATION EXTRACTION SENTENCE CLASSIFICATION
Ranked #1 on Semantic Similarity on BIOSSES
DOCUMENT CLASSIFICATION DRUG–DRUG INTERACTION EXTRACTION MEDICAL NAMED ENTITY RECOGNITION MEDICAL RELATION EXTRACTION NATURAL LANGUAGE INFERENCE RELATION EXTRACTION SEMANTIC SIMILARITY TRANSFER LEARNING
Deep neural network models have recently achieved state-of-the-art performance gains in a variety of natural language processing (NLP) tasks (Young, Hazarika, Poria, & Cambria, 2017).
We also investigate the effects of a small amount of additional pretraining on PubMed content, and of combining FLAIR and ELMO models.
Ranked #1 on Named Entity Recognition on Species-800 (using extra training data)
The overwhelming amount of biomedical scientific texts calls for the development of effective language models able to tackle a wide range of biomedical natural language processing (NLP) tasks.
Ranked #1 on Named Entity Recognition on BC5CDR (using extra training data)
State-of-the-art studies have demonstrated the superiority of joint modelling over pipeline implementation for medical named entity recognition and normalization due to the mutual benefits between the two processes.
Functioning is gaining recognition as an important indicator of global health, but remains under-studied in medical natural language processing research.