That’s an interesting question, Padmakumar !

I think the following community members may be able to help 

@Zaid Chougle 

@Marc Mueller 

@madhu subbegowda 

Hi ​@Padmakumar 

1. Dataset Size Calibration
   • Q: When is a dataset "too small" or "too large" for NER?
     • Too Small: A dataset is considered too small if your model fails to generalize or exhibits high variance. Signs include:
       • High training accuracy but low validation/test performance.
       • Low F1 score, especially on rare entity types.
     • Too Large: A dataset is too large when:
       • Additional data doesn’t lead to improved performance (diminishing returns).
       • Training cost/time increases without performance gains.
   • Q: What other metrics help assess optimal size?
     • Learning Curves: Plot F1 (or loss) vs. dataset size.
     • Data efficiency scores (like marginal gain per 1k examples).
     • Validation loss stagnation.
     • Entity coverage: Check whether all entity types and linguistic patterns are represented.
2. Data Preparation Techniques
   • Q: Tools for automating NER ground truth labeling?
     • Prodi.gy: Active learning with manual annotation.
     • Label Studio: Versatile, with NER-specific workflows.
     • Snorkel: Weak supervision through labeling functions.
     • Doccano: Open-source and easy to integrate.
   • Q: Handling ambiguous labeling cases?
     • Create labeling guidelines with edge-case documentation.
     • Use inter-annotator agreement (IAA) metrics like Cohen’s Kappa.
     • Add "Uncertain" tags or confidence levels.
     • Regular annotation audits or consensus rounds.
3. Dataset Diversity
   • Q: How to quantify and evaluate diversity?
     • Type-token ratio of entity classes.
     • Entropy measures on label distributions.
     • POS/NER co-occurrence diversity.
     • Embedding-based clustering (e.g., UMAP on sentence embeddings) to detect thematic variance.
   • Q: Ensuring diversity without adding noise?
     • Use stratified sampling across domains/sources.
     • Apply domain-specific filters to eliminate irrelevant text.
     • Regular manual review of new sources for content quality.
4. Model Evaluation
   • Q: Model behavior on unseen dataset classes?
     • Generally, poor generalization—NER models need exposure to class patterns.
     • Use few-shot learning strategies and zero-shot models like TARS-NER or FLAN.
   • Q: Minimizing class imbalance?
     • Oversample rare classes.
     • Use weighted loss functions (e.g., focal loss).
     • Augment samples via entity-level substitution or paraphrasing.
   • Q: Use of public benchmarks?
     • Yes, evaluate on CoNLL-2003, OntoNotes, WNUT for baseline comparisons.
     • Use cross-dataset evaluation to test generalizability.
5. Real-World Applications
   • Q: Including rare but important classes?
     • Collaborate with domain experts to curate critical entity lists.
     • Use distant supervision from structured knowledge bases.
     • Apply entity bootstrapping to expand rare-class coverage.
   • Q: Varying guidelines for sentiment analysis or other tasks?
     • Sentiment labels are more subjective and context-dependent.
     • Ambiguity and label noise are higher—need clearer guidelines and possibly ordinal labels.
     • Entity extraction needs fine-grained boundaries, while sentiment leans on semantic polarity.
6. Advanced Techniques
   • Q: Role of active learning and transfer learning?
     • Active Learning: Prioritize uncertain samples for labeling (e.g., uncertainty sampling, entropy sampling).
     • Transfer Learning: Start with models like BERT fine-tuned on large NER corpora, then fine-tune on your domain.
     • Drastically reduces the data required.
   • Q: Impact of contextual embeddings?
     • Contextual embeddings (BERT, RoBERTa) reduce reliance on large annotated datasets.
     • Better generalization to ambiguous contexts and rare words.
     • Handle word sense disambiguation and polysemy effectively.

Let me know if this answers your questions.