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NLP-NER

A study of Named Entity Recognition on Twitter data (TweeBank_NER), examining whether model errors and uncertainty align with places where human annotators disagree.

Requirements

Install dependencies with:

pip install torch transformers seqeval scikit-learn numpy

Data

All data files are in the data/ folder:

  • train.jsonl, dev.jsonl, test.jsonl — official TweeBank_NER splits
  • Hjalte_500.jsonl, mar_500.jsonl, val_500.jsonl — manual annotations of the first 500 test tweets by each group member
  • test_pred_base.jsonl — baseline BERT predictions on the test set
  • bertweet_pred.jsonl — BERTweet predictions on the first 500 test tweets

Running the code

All scripts should be run from the code/ directory.

cd code

1. Train the baseline model

Fine-tunes bert-base-cased on the TweeBank training data and saves predictions on the test set to test_pred_base.jsonl.

python3 train-base.py

2. Run BERTweet inference

Runs the pretrained bertweet-tb2-ner model on the test set and saves predictions to bertweet_pred_test.jsonl.

python3 bertweet.py

3. Compute Inter-Annotator Agreement (IAA)

Computes pairwise F1 between the three annotators for each entity type (PER, LOC, ORG, MISC).

python3 IAA.py

4. Evaluate F1 scores

Computes span-level F1 (strict, unlabeled, and loose) for both models and both human annotators against the gold standard.

python3 F1.py

5. Annotation comparison and confusion matrices

Open and run data/ann_comparison.ipynb in Jupyter to reproduce the disagreement analysis and confusion matrix figures.

jupyter notebook data/ann_comparison.ipynb