تقرير
CoNLL#: Fine-grained Error Analysis and a Corrected Test Set for CoNLL-03 English
| العنوان: | CoNLL#: Fine-grained Error Analysis and a Corrected Test Set for CoNLL-03 English |
|---|---|
| المؤلفون: | Rueda, Andrew, Mellado, Elena Álvarez, Lignos, Constantine |
| المصدر: | Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). 3718-3728 |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence |
| الوصف: | Modern named entity recognition systems have steadily improved performance in the age of larger and more powerful neural models. However, over the past several years, the state-of-the-art has seemingly hit another plateau on the benchmark CoNLL-03 English dataset. In this paper, we perform a deep dive into the test outputs of the highest-performing NER models, conducting a fine-grained evaluation of their performance by introducing new document-level annotations on the test set. We go beyond F1 scores by categorizing errors in order to interpret the true state of the art for NER and guide future work. We review previous attempts at correcting the various flaws of the test set and introduce CoNLL#, a new corrected version of the test set that addresses its systematic and most prevalent errors, allowing for low-noise, interpretable error analysis. Comment: Accepted to LREC-COLING 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.11865 |
| رقم الأكسشن: | edsarx.2405.11865 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |