Description

This project uses an existing corpus of newspaper articles to derive models that identify (1) whether a MIC occurred, (2) what type of militarized interaction was described, (3) whether fatalities occurred, and (4) the range of fatalities, if any. The newspaper articles have been retrieved based on boolean search terms associated with militarized conflict. However, more than 95% of the articles are false positives for identifying MICs.

Duration

Total project length: 175 hours

Task ideas

• Extract and preprocess textual data from the newspaper corpora for NLP analysis.
• Develop and train NLP models, such as LSTM or BERT, to understand and code the militarized actions and fatalities in each conflict.

Expected results

• The successful project will identify newspaper articles that contain MICs and retrieve the conflict participants (initiator and target), the militarized action, and whether how many fatalities (range) occurred.

Requirements

The project requires the ability to code in Python and knowledge of machine learning and natural language processing.

Project difficulty level

This project has a moderate level of difficulty.

Mentors

• Douglas M. Gibler (University of Alabama)
• Piyush Pawar (University of Alabama)
• Jean-Christophe Boucher (University of Calgary)

Please DO NOT contact mentors directly by email. Instead, please email human-ai@cern.ch with Project Title and include your CV and test results. The mentors will then get in touch with you.

Corresponding Project

• ISSR

Participating Organizations

• Alabama

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