arXiv:2107.14154
공식 코드 · Papers with Code
SeqScore: Addressing Barriers to Reproducible Named Entity Recognition Evaluation
원문 초록
To address a looming crisis of unreproducible evaluation for named entity recognition, we propose guidelines and introduce SeqScore, a software package to improve reproducibility. The guidelines we propose are extremely simple and center around transparency regarding how chunks are encoded and scored. We demonstrate that despite the apparent simplicity of NER evaluation, unreported differences in the scoring procedure can result in changes to scores that are both of noticeable magnitude and statistically significant. We describe SeqScore, which addresses many of the issues that cause replication failures.
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Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models
Label-Guided In-Context Learning for Named Entity Recognition
Named Entity Recognition in Historical Italian: The Case of Giacomo Leopardi's Zibaldone
A Comparative Analysis of Static Word Embeddings for Hungarian
Methods for Recognizing Nested Terms
Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents
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