AutoSpecNER is a new fine-grained NER dataset for vehicle advertisements with 659 examples and 15 categories, where DeBERTa reaches 90% micro-F1 versus 43% for rules and 77.8% for the best LLM.
Agreement, the f-measure, and reliability in infor- mation retrieval
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CL 2years
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UNVERDICTED 2representative citing papers
With sufficient training data, the unsupervised RhymeTagger outperforms human inter-annotator agreement in rhyme recognition across seven languages while LLMs without phonetic awareness perform poorly.
citing papers explorer
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AutoSpecNER: A Fine-Grained Named Entity Recognition Dataset for Vehicle Specification Extraction
AutoSpecNER is a new fine-grained NER dataset for vehicle advertisements with 659 examples and 15 categories, where DeBERTa reaches 90% micro-F1 versus 43% for rules and 77.8% for the best LLM.
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Training Data Size Sensitivity in Unsupervised Rhyme Recognition
With sufficient training data, the unsupervised RhymeTagger outperforms human inter-annotator agreement in rhyme recognition across seven languages while LLMs without phonetic awareness perform poorly.