Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Biomedical Named Entity Recognition at Scale   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.

3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an