Bangla-Complex-Named-Entity-Recognition-Challenge

Winning Solution for the Bangla Complex Named Entity Recognition Challenge - BDOSN NLP Hackathon 2023 [arxiv]

Dataset

The provided dataset was a labeled Bangla NER dataset in the ċonll format where each word had a corresponding NER tag and sentences were separated with empty lines. The train set consists of 15300 sentences and the validation set has 800 sentences. The length of the sentences in both sets varies from 2 words to 35 words with the average length being 12 words. There are 7 different NER tags in the given dataset.

Presence of CW, PROD, CORP and GRP tags in the dataset makes the task challenging.

The dataset is available in the data folder.

Approach

The competition had two tracks, one was a DL based track and the other was a feature based track. We participated in both the tracks and our solution for the DL based track was based on the Bangla BERT architecture and our solution for the feature based track was based on the CRF architecture.

Read the [arxiv report] for more details.

Results

Feature Based Track

DL Based Track

Reproducing the Results

Running DL Model

Normalizer (Required)

$ pip install git+https://github.com/csebuetnlp/normalizer

New Data

https://multiconer.github.io/competition

2023 Train and Dev Datasets (about 10K)

train_inference.py:

The train_file_path, validation_file_path need to be set inside the main function and the varialble 'train' need to be set to True to train.

Running Feature Based Model

bangla-crf-baseline.ipynb and bangla-crf-with-kmeans-and-gazetteer.ipynb:

The files included in the data folder should remain in a relative path "../data" for running the notebooks.

Citation

[arxiv]

@misc{shahgir2023banglaconer,
      title={BanglaCoNER: Towards Robust Bangla Complex Named Entity Recognition}, 
      author={HAZ Sameen Shahgir and Ramisa Alam and Md. Zarif Ul Alam},
      year={2023},
      eprint={2303.09306},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}