Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Few-shot Named Entity Recognition with Cloze Questions   Despite the huge and continuous advances in computational linguistics, the
lack of annotated data for Named Entity Recognition (NER) is still a
challenging issue, especially in low-resource languages and when domain
knowledge is required for high-quality annotations. Recent findings in NLP show
the effectiveness of cloze-style questions in enabling language models to
leverage the knowledge they acquired during the pre-training phase. In our
work, we propose a simple and intuitive adaptation of Pattern-Exploiting
Training (PET), a recent approach which combines the cloze-questions mechanism
and fine-tuning for few-shot learning: the key idea is to rephrase the NER task
with patterns. Our approach achieves considerably better performance than
standard fine-tuning and comparable or improved results with respect to other
few-shot baselines without relying on manually annotated data or distant
supervision on three benchmark datasets: NCBI-disease, BC2GM and a private
Italian biomedical corpus.

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The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
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The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
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The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: q-bio
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The categories of this paper are: funct-an