Biomedical Named Entity Recognition at Scale

• URL: http://arxiv.org/abs/2011.06315v1
• Date: Thu, 12 Nov 2020 11:10:17 GMT
• Title: Biomedical Named Entity Recognition at Scale
• Authors: Veysel Kocaman and David Talby
• Abstract summary: We present a single trainable NER model that obtains new state-of-the-art results on seven public biomedical benchmarks. This model is freely available within a production-grade code base as part of the open-source Spark NLP library.
• Score: 6.85316573653194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Named entity recognition (NER) is a widely applicable natural language processing task and building block of question answering, topic modeling, information retrieval, etc. In the medical domain, NER plays a crucial role by extracting meaningful chunks from clinical notes and reports, which are then fed to downstream tasks like assertion status detection, entity resolution, relation extraction, and de-identification. Reimplementing a Bi-LSTM-CNN-Char deep learning architecture on top of Apache Spark, we present a single trainable NER model that obtains new state-of-the-art results on seven public biomedical benchmarks without using heavy contextual embeddings like BERT. This includes improving BC4CHEMD to 93.72% (4.1% gain), Species800 to 80.91% (4.6% gain), and JNLPBA to 81.29% (5.2% gain). In addition, this model is freely available within a production-grade code base as part of the open-source Spark NLP library; can scale up for training and inference in any Spark cluster; has GPU support and libraries for popular programming languages such as Python, R, Scala and Java; and can be extended to support other human languages with no code changes.

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