Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First --
  Using Relation Extraction to Identify Entities   In this paper, we present an end-to-end joint entity and relation extraction
approach based on transformer-based language models. We apply the model to the
task of linking mathematical symbols to their descriptions in LaTeX documents.
In contrast to existing approaches, which perform entity and relation
extraction in sequence, our system incorporates information from relation
extraction into entity extraction. This means that the system can be trained
even on data sets where only a subset of all valid entity spans is annotated.
We provide an extensive evaluation of the proposed system and its strengths and
weaknesses. Our approach, which can be scaled dynamically in computational
complexity at inference time, produces predictions with high precision and
reaches 3rd place in the leaderboard of SemEval-2022 Task 12. For inputs in the
domain of physics and math, it achieves high relation extraction macro F1
scores of 95.43% and 79.17%, respectively. The code used for training and
evaluating our models is available at: https://github.com/nicpopovic/RE1st

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The categories of this paper are: cond-mat
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The categories of this paper are: supr-con
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: stat
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The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
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The categories of this paper are: q-bio
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The categories of this paper are: acc-phys
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The categories of this paper are: physics
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The categories of this paper are: adap-org
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The categories of this paper are: econ
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th
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The categories of this paper are: nlin