CEHA: A Dataset of Conflict Events in the Horn of Africa
Natural Language Processing (NLP) of news articles can play an important role in understanding the dynamics and causes of violent conflict. Despite the availability of datasets categorizing various conflict events, the existing labels often do not cover all of the fine-grained violent conflict event...
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| creator | Bai, Rui Lu, Di Ran, Shihao Olson, Elizabeth Lamba, Hemank Cahill, Aoife Tetreault, Joel Jaimes, Alex |
| description | Natural Language Processing (NLP) of news articles can play an important role
in understanding the dynamics and causes of violent conflict. Despite the
availability of datasets categorizing various conflict events, the existing
labels often do not cover all of the fine-grained violent conflict event types
relevant to areas like the Horn of Africa. In this paper, we introduce a new
benchmark dataset Conflict Events in the Horn of Africa region (CEHA) and
propose a new task for identifying violent conflict events using online
resources with this dataset. The dataset consists of 500 English event
descriptions regarding conflict events in the Horn of Africa region with
fine-grained event-type definitions that emphasize the cause of the conflict.
This dataset categorizes the key types of conflict risk according to specific
areas required by stakeholders in the Humanitarian-Peace-Development Nexus.
Additionally, we conduct extensive experiments on two tasks supported by this
dataset: Event-relevance Classification and Event-type Classification. Our
baseline models demonstrate the challenging nature of these tasks and the
usefulness of our dataset for model evaluations in low-resource settings with
limited number of training data. |
| doi_str_mv | 10.48550/arxiv.2412.13511 |
| format | Article |
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in understanding the dynamics and causes of violent conflict. Despite the
availability of datasets categorizing various conflict events, the existing
labels often do not cover all of the fine-grained violent conflict event types
relevant to areas like the Horn of Africa. In this paper, we introduce a new
benchmark dataset Conflict Events in the Horn of Africa region (CEHA) and
propose a new task for identifying violent conflict events using online
resources with this dataset. The dataset consists of 500 English event
descriptions regarding conflict events in the Horn of Africa region with
fine-grained event-type definitions that emphasize the cause of the conflict.
This dataset categorizes the key types of conflict risk according to specific
areas required by stakeholders in the Humanitarian-Peace-Development Nexus.
Additionally, we conduct extensive experiments on two tasks supported by this
dataset: Event-relevance Classification and Event-type Classification. Our
baseline models demonstrate the challenging nature of these tasks and the
usefulness of our dataset for model evaluations in low-resource settings with
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in understanding the dynamics and causes of violent conflict. Despite the
availability of datasets categorizing various conflict events, the existing
labels often do not cover all of the fine-grained violent conflict event types
relevant to areas like the Horn of Africa. In this paper, we introduce a new
benchmark dataset Conflict Events in the Horn of Africa region (CEHA) and
propose a new task for identifying violent conflict events using online
resources with this dataset. The dataset consists of 500 English event
descriptions regarding conflict events in the Horn of Africa region with
fine-grained event-type definitions that emphasize the cause of the conflict.
This dataset categorizes the key types of conflict risk according to specific
areas required by stakeholders in the Humanitarian-Peace-Development Nexus.
Additionally, we conduct extensive experiments on two tasks supported by this
dataset: Event-relevance Classification and Event-type Classification. Our
baseline models demonstrate the challenging nature of these tasks and the
usefulness of our dataset for model evaluations in low-resource settings with
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in understanding the dynamics and causes of violent conflict. Despite the
availability of datasets categorizing various conflict events, the existing
labels often do not cover all of the fine-grained violent conflict event types
relevant to areas like the Horn of Africa. In this paper, we introduce a new
benchmark dataset Conflict Events in the Horn of Africa region (CEHA) and
propose a new task for identifying violent conflict events using online
resources with this dataset. The dataset consists of 500 English event
descriptions regarding conflict events in the Horn of Africa region with
fine-grained event-type definitions that emphasize the cause of the conflict.
This dataset categorizes the key types of conflict risk according to specific
areas required by stakeholders in the Humanitarian-Peace-Development Nexus.
Additionally, we conduct extensive experiments on two tasks supported by this
dataset: Event-relevance Classification and Event-type Classification. Our
baseline models demonstrate the challenging nature of these tasks and the
usefulness of our dataset for model evaluations in low-resource settings with
limited number of training data.</abstract><doi>10.48550/arxiv.2412.13511</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computation and Language |
| title | CEHA: A Dataset of Conflict Events in the Horn of Africa |
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