MIC Test Documentation

Overview

This document outlines the process of extracting key entities such as dates, fatalities, and involved countries from textual articles related to Militarized Interstate Confrontations (MICs). The methodology involves text preprocessing, Named Entity Recognition (NER) using a fine-tuned RoBERTa model, and iterative improvements to enhance accuracy.

Steps

Step 1: Text Cleaning -Preprocess the some input articles by removing unnecessary characters, stopwords, and formatting issues.

-Normalize the text to ensure consistency in further processing.

Step 2: BIO Tagging with spaCy

-Utilize a pre-trained spaCy model to perform BIO (Beginning-Inside-Outside) tagging on the cleaned text.

-Convert the annotated data into a structured JSON format for further processing.

Step 3: Fine-Tuning RoBERTa for NER

-Use the BIO-tagged JSON file to fine-tune the RoBERTa model for Named Entity Recognition (NER).

-Train the model to recognize key MIC-related entities, including dates, fatalities, and involved countries.

Step 4: Additional Training for Improved Performance

-Train the fine-tuned model on a larger dataset to improve its accuracy and generalization capabilities.

-Ensure that the model adapts to diverse text formats and entity variations.

Step 5: Entity Extraction from Articles

-Utilize the trained model to extract the following entities from MIC-related articles:

-Dates (mentioning specific timeframes of events)

-Fatalities (number of casualties in the incidents)

-Involved Countries (parties engaged in the conflict)

Model Improvement Strategies

-To achieve more precise outputs, continue training the model on an expanded dataset.

-Improve the tagging methodology to enhance entity recognition accuracy.

Conclusion

By following this structured approach, we can systematically extract and analyze key information from MIC-related articles, facilitating better insights into historical and ongoing militarized confrontations.