One post tagged with "Natural Language Processing"

Overview​

This project aims to explore methodologies related to Named Entity Recognition.

Methodology​

The project evaluated three distinct deep learning architectures for Named Entity Recognition (NER):

• SpaCy: (Responsible: Square) Explored the key approaches used in the SpaCy library, including hash embeddings and trasition-based models.
• BERT (Bidirectional Encoder Representations from Transformers): (Responsible: Sam/Ash) Fine-tuned the bert-base-cased model over NER task, with following modifications:
  • Aggregation Strategies: Improved aggregation method to make sure that each input word is not split into parts in the output.
  • Masking: Masked by chance 15% of named entities in the training data and adjusted the leraning rate and number of epochs for the fine-tuning process to test if encouraging the utilization of nearby context could improve the generalizability over unseen named-entities.
• Gemma 3 (Decoder LLM): (Responsible: Square) Tested the performance by using decoder LLMs on NER tasks, guided using prompt engineering. Ensured JSON output. Evaluated on both zero-shot (only instructions) and few-shot (instructions plus examples) cases.

For more experimental data, please see the report for more information.