This minimizes the human supervision cost and allows nonprofits to access a broader range of conflict data sources to reduce reporting bias. Our model drastically reduces the time spent on manual data transformations and improves armed conflict event classification by identifying multiple incidence types. In this paper, we explore the use of a deep natural language processing (NLP) model to aid the transformation of armed conflict data for conflict analysis. Transformation of armed conflict data tends to be a manual, time-consuming task that nonprofits with limited budgets struggle to keep up with. Due to the lack of a standardized approach to collating conflict data, publicly available armed conflict datasets often require manipulation depending upon the needs of end users. Conflict resolution practitioners consistently struggle with access to structured armed conflict data, a dataset already rife with uncertainty, inconsistency, and politicization.
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