FinTagging decomposes XBRL tagging into FinNI extraction and FinCL full-taxonomy linking, showing LLMs handle extraction but struggle with fine-grained concept alignment in zero-shot settings.
CoRR abs/1911.03814 (2019)
4 Pith papers cite this work, alongside 20 external citations. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Entity representations learned from text via link prediction generalize to unseen entities and transfer to classification and retrieval with reported gains of 22% MRR, 16% accuracy, and 8.8% NDCG@10.
ELERAG integrates Wikidata entity linking with hybrid RRF re-ranking into RAG and outperforms baselines on a custom Italian academic dataset while cross-encoder methods win on the general SQuAD-it dataset.
LELA is extended to an end-to-end LLM-based entity linking system with integrated zero-shot NER and domain adaptation, validated for performance and robustness across diverse settings.
citing papers explorer
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FinTagging: Benchmarking LLMs for Extracting and Structuring Financial Information
FinTagging decomposes XBRL tagging into FinNI extraction and FinCL full-taxonomy linking, showing LLMs handle extraction but struggle with fine-grained concept alignment in zero-shot settings.
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Inductive Entity Representations from Text via Link Prediction
Entity representations learned from text via link prediction generalize to unseen entities and transfer to classification and retrieval with reported gains of 22% MRR, 16% accuracy, and 8.8% NDCG@10.
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Enhancing Retrieval-Augmented Generation with Entity Linking for Educational Platforms
ELERAG integrates Wikidata entity linking with hybrid RRF re-ranking into RAG and outperforms baselines on a custom Italian academic dataset while cross-encoder methods win on the general SQuAD-it dataset.
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LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation
LELA is extended to an end-to-end LLM-based entity linking system with integrated zero-shot NER and domain adaptation, validated for performance and robustness across diverse settings.