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A Survey on Recent Advances in Named Entity Recognition from Deep Learning models

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arxiv 1910.11470 v1 pith:ZAD4RW2Z submitted 2019-10-25 cs.CL cs.LG

Vikas Yadav , Steven Bethard This is my paper · ORCID
classification cs.CLcs.LG
keywords systemsdeepneuralbeenentityimprovementslearningnamed
verification ladder T0 review T1 audit T2 compute T3 formal
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Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but accurate systems using deep neural networks (NN) have only been introduced in the last few years. We present a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms. Our results highlight the improvements achieved by neural networks, and show how incorporating some of the lessons learned from past work on feature-based NER systems can yield further improvements.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 468 citations worldwide. Full citation record

  1. A Multi-way Parallel Named Entity Annotated Corpus for English, Tamil and Sinhala

    cs.CL 2024-12 conditional novelty 7.0 of 10

    A new manually annotated English-Tamil-Sinhala parallel NER corpus of 3,835 sentences per language, with benchmarks showing XLM-R outperforms monolingual and Indic models, and a case study where NER output improves En...

  2. BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering

    cs.CL 2025-07 reject novelty 6.0 of 10

    BYOKG-RAG combines LLM-generated entities, paths, queries, and candidate answers with multiple graph retrieval tools to answer questions over custom knowledge graphs without training data.

  3. The Use of Artificial Intelligence in Military Intelligence: An Experimental Investigation of Added Value in the Analysis Process

    cs.AI 2024-12 conditional novelty 6.0 of 10

    Using AI search, summarization, and named entity recognition together improved factual military analysis scores under time pressure, but did not increase analysts' confidence.

  4. GerPS-Compare: Comparing NER methods for legal norm analysis

    cs.CL 2024-12 conditional novelty 4.0 of 10

    On a German legal norm corpus, a fine-tuned XLM-RoBERTa outperforms a rule-based system and a prompted LLM (LeoLM) in macro F1 for ten annotation classes.

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