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. Abstract : 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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