Fine-Tuning GLiNER for Location Mention Recognition (LMR)

Project Description: Improving Location Detection in Text with Fine-Tuned NER

Named Entity Recognition (NER) is crucial for extracting structured information from unstructured text. Location Mention Recognition (LMR), a specific subtask of NER, focuses on identifying geographical locations mentioned in text. This project enhances LMR capabilities, particularly in noisy user-generated content like social media posts, by fine-tuning GLiNER, a state-of-the-art generative NER model. Accurate LMR is vital for applications such as disaster response, event tracking, and location-based services.

Objective

The primary objective is to significantly improve the performance (precision, recall, F1-score) of Location Mention Recognition by fine-tuning the GLiNER model on datasets relevant to user-generated content and disaster scenarios. The goal is to create a model adept at identifying location entities even in informal, abbreviated, or misspelled text common in social media.

Key Features

• Advanced NER Model: Leverages GLiNER (Generalist Linking NER), a powerful generative model capable of recognizing arbitrary entity types without predefined labels during inference.
• Fine-Tuning for LMR: Adapts the pre-trained GLiNER model specifically for the task of identifying location mentions, enhancing its specialization.
• Focus on User-Generated Content: Trains and evaluates the model on datasets containing noisy text (e.g., tweets, forum posts) to ensure robustness in real-world scenarios.
• Application-Driven: Tailored to improve downstream tasks like mapping disaster reports, tracking population movements, or enhancing location-aware recommendations.

Methodology

1. Data Understanding and Preparation:

   • Select and preprocess relevant datasets containing location mentions, particularly from social media or disaster-related corpora.
   • The dataset used follows the format:

     [
         {
             "tokens": [...],
             "ner": [[start_index, end_index, "LOC"]],
             "label": ["location"]
         }
     ]
     

   • Pre-processing ensured all entries were clean, well-formatted, and ready for model ingestion.

2. Model Selection and Fine-Tuning:

   • Model Choice: Fine-tuned a large GLiNER model, specifically the urchade/gliner_large-v2.1, which is well-suited for NER tasks involving location detection.
   • Set up the GLiNER model architecture using Hugging Face Transformers.
   • Implement a fine-tuning strategy, involving supervised fine-tuning with labeled LMR data.
   • Experiment with different hyperparameters and training techniques to optimize performance.

3. Training Setup:

   • Training Configuration:
     • The model was trained for 5 epochs, using a constant learning rate of 1e-6 and batch size of 8.
     • Regular evaluation checkpoints monitored performance on the test dataset.
     • Model checkpoints were saved periodically, with the best-performing model loaded at the end.
   • Optimization Techniques:
     • Weight Decay: Applied during fine-tuning to prevent overfitting.
     • Gradient accumulation was used to manage memory on a CUDA-enabled GPU.

4. Evaluation and Performance Monitoring:

   • Evaluate the fine-tuned model rigorously against baseline GLiNER and other NER models on LMR benchmark datasets, focusing on metrics relevant to location extraction accuracy.
   • The training progress and metrics were logged using Weights and Biases (W&B) for detailed analysis.
   • Analyze model errors to identify areas for further improvement, particularly concerning informal language and ambiguity.

Tools and Technologies

• Core Concepts: Named Entity Recognition (NER), Location Mention Recognition (LMR), Natural Language Processing (NLP), Generative AI, Fine-Tuning
• Models: GLiNER (and potentially other baseline NER models like spaCy, BERT-NER)
• Frameworks/Libraries: Hugging Face Transformers, PyTorch, Weights and Biases (W&B)
• Datasets: Publicly available NER/LMR datasets (e.g., CoNLL, WNUT) or custom datasets curated from social media APIs (e.g., Twitter API).

Outcome

• Improved Entity Recognition: After fine-tuning, the GLiNER model showed significant improvement in recognizing location mentions in noisy and structured datasets.
• Efficiency in Resource Utilization: The model was trained on a CUDA-enabled GPU, allowing it to process large amounts of data without running into memory constraints.

Applications

• Disaster Response: Rapidly identifying locations mentioned in emergency calls or social media posts to map affected areas and direct aid.
• Geospatial Analysis: Extracting place names from text documents (news articles, reports) for geographic information systems (GIS).
• Event Detection: Monitoring social media for mentions of events happening at specific locations.
• Location-Based Services: Enhancing recommendation systems or targeted advertising based on locations discussed by users.

Future Work

1. Expand Dataset Coverage:
   • Include more diverse text sources, such as news articles and forum posts, to improve the model’s versatility.
2. Model Enhancement for Ambiguity Handling:
   • Further fine-tuning to reduce errors related to ambiguous location names (e.g., “Paris” as a city versus a person’s name) by incorporating context-aware training techniques.

Code: The full implementation can be found in the repository linked here.