Summary of Conll#: Fine-grained Error Analysis and a Corrected Test Set For Conll-03 English, by Andrew Rueda et al.
CoNLL#: Fine-grained Error Analysis and a Corrected Test Set for CoNLL-03 English
by Andrew Rueda, Elena Álvarez Mellado, Constantine Lignos
First submitted to arxiv on: 20 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper tackles the stagnation in named entity recognition (NER) performance on the CoNLL-03 English dataset by conducting a fine-grained evaluation of top-performing NER models. The authors introduce new document-level annotations to assess their performance and categorize errors for interpretation. They review previous attempts at correcting test set flaws and propose CoNLL#, a corrected version addressing systematic errors, enabling low-noise error analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks into why the best named entity recognition systems haven’t gotten much better lately on one important dataset. The researchers take a close look at what these top systems get wrong by adding more information to the test set. They then sort mistakes into categories so we can understand how well these systems are really doing and figure out where to go from here. |
Keywords
» Artificial intelligence » Named entity recognition » Ner
