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A Survey on Recent Advances in Named Entity Recognition from Deep Learning models
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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 7 Pith papers
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A Multi-way Parallel Named Entity Annotated Corpus for English, Tamil and Sinhala
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...
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BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering
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.
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The Use of Artificial Intelligence in Military Intelligence: An Experimental Investigation of Added Value in the Analysis Process
Using AI search, summarization, and named entity recognition together improved factual military analysis scores under time pressure, but did not increase analysts' confidence.
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Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations
The paper introduces UES and NPE, a framework that treats unlabeled-entity and noisy-entity problems separately in distantly supervised NER, and reports average F1 gains over prior baselines.
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Uncovering Conspiratorial Narratives within Arabic Online Content
Arabic online conspiracy discourse clusters into six narrative categories (gender/feminist, geopolitical, government cover-ups, apocalyptic, Judeo-Masonic, geoengineering) according to NER and Top2Vec analysis of 1,64...
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GerPS-Compare: Comparing NER methods for legal norm analysis
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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A Survey on Retrieval And Structuring Augmented Generation with Large Language Models
The paper presents a comprehensive survey and taxonomy of RAS methods, covering retrieval, text structuring, and LLM integration.
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