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New research explores unsupervised methods for Named Entity Recognition with limited data

This paper investigates unsupervised methods for Named Entity Recognition (NER) when dealing with small or unlabeled datasets across multiple domains. It proposes using unsupervised pre-training to identify entities without annotations, followed by transfer learning on limited datasets. The research addresses challenges like domain variability and data sparsity by exploring techniques such as data augmentation, few-shot learning, and domain adversarial training to improve NER system performance and adaptability. AI

IMPACT This research could lead to more efficient and adaptable NLP applications in domains with limited annotated data.

RANK_REASON The cluster contains two identical arXiv preprints detailing a research paper on NLP methods.

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research explores unsupervised methods for Named Entity Recognition with limited data

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The cluster contains two identical arXiv preprints detailing a research paper on NLP methods.
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55 days old
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Israel Fianyi, James Montgomery, Soonja Yeom ·

    Unsupervised Multidomain Approaches to Named Entity Recognition with Small Datasets

    arXiv:2608.00984v1 Announce Type: new Abstract: This paper explores the challenges and the methodologies associated with learning quality representations in scenarios with unlabelled small or limited datasets for downstream information extraction task (Multidomain Named Entity Re…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Soonja Yeom ·

    Unsupervised Multidomain Approaches to Named Entity Recognition with Small Datasets

    This paper explores the challenges and the methodologies associated with learning quality representations in scenarios with unlabelled small or limited datasets for downstream information extraction task (Multidomain Named Entity Recognition (NER). The study adopts a Transfer Lea…