mlsanityData to 7 Oct 2026

Classifying Dyads for Militarized Conflict Analysis

arXiv 2109.12860 ↗First posted 27 Sep 2021cs.CL cs.LG cs.SIcited by 5

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Abstract (arXiv)

Understanding the origins of militarized conflict is a complex, yet important undertaking. Existing research seeks to build this understanding by considering bi-lateral relationships between entity pairs (dyadic causes) and multi-lateral relationships among multiple entities (systemic causes). The aim of this work is to compare these two causes in terms of how they correlate with conflict between two entities. We do this by devising a set of textual and graph-based features which represent each of the causes. The features are extracted from Wikipedia and modeled as a large graph. Nodes in this graph represent entities connected by labeled edges representing ally or enemy-relationships. This allows casting the problem as an edge classification task, which we term dyad classification. We propose and evaluate classifiers to determine if a particular pair of entities are allies or enemies. Our results suggest that our systemic features might be slightly better correlates of conflict. Further, we find that Wikipedia articles of allies are semantically more similar than enemies.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 12 of its 49 references that are in the map. First posted on arXiv 27 Sep 2021.

Builds on

12 of 49 references.

References · 12

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Cited by year

Citing papers per year: 2022: 3; 2023: 2.

5 papers.

  1. 2022An Ordinal Latent Variable Model of Conflict Intensitycited by 6
  2. 2022Rethinking the Event Coding Pipeline with Prompt Entailmentcited by 5
  3. 2022Disentangling Active and Passive Cosponsorship in the U.S. Congresscited by 4
  4. 2023A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extractioncited by 2
  5. 2023Extracting Victim Counts from Textcited by 0