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Han, Xiaochuang; Choi, Eunsol; Tan, Chenhao

doi:10.18653/v1/n19-1167

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…Stoehr et al (2023b) build on those models by further imposing an ordering on their latent space, which captures conflictcooperation intensity. Another line of work models friend-enemy relationship trajectories using neural network-based (Han et al, 2019) or hidden Markov model-based (Chaturvedi et al, 2017) approaches.…”

Section: Related Workmentioning

confidence: 99%

An Ordinal Latent Variable Model of Conflict Intensity

Stoehr¹,

Hennigen²,

Valvoda³

et al. 2023

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Measuring the intensity of events is crucial for monitoring and tracking armed conflict. Advances in automated event extraction have yielded massive data sets of "who did what to whom" micro-records that enable datadriven approaches to monitoring conflict. The Goldstein scale is a widely-used expert-based measure that scores events on a conflictualcooperative scale. It is based only on the action category ("what") and disregards the subject ("who") and object ("to whom") of an event, as well as contextual information, like associated casualty count, that should contribute to the perception of an event's "intensity". To address these shortcomings, we take a latent variablebased approach to measuring conflict intensity. We introduce a probabilistic generative model that assumes each observed event is associated with a latent intensity class. A novel aspect of this model is that it imposes an ordering on the classes, such that higher-valued classes denote higher levels of intensity. The ordinal nature of the latent variable is induced from naturally ordered aspects of the data (e.g., casualty counts) where higher values naturally indicate higher intensity. We evaluate the proposed model both intrinsically and extrinsically, showing that it obtains good held-out predictive performance.

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Section: Related Workmentioning

confidence: 99%

An Ordinal Latent Variable Model of Conflict Intensity

Stoehr¹,

Hennigen²,

Valvoda³

et al. 2023

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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“…We use the contrastive maxmargin objective function following previous works in dictionary learning (Iyyer et al, 2016;Frermann and Szarvas, 2017;Han et al, 2019). We randomly sample a set of negative samples (N − ) with the same view as the current input from the mini-batch.…”

Section: Unsupervised Objective Contrastive Lossmentioning

confidence: 99%

Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames

Khanehzar¹,

Cohn²,

Mikołajczak³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Understanding how news media frame political issues is important due to its impact on public attitudes, yet hard to automate. Computational approaches have largely focused on classifying the frame of a full news article while framing signals are often subtle and local. Furthermore, automatic news analysis is a sensitive domain, and existing classifiers lack transparency in their predictions. This paper addresses both issues with a novel semi-supervised model, which jointly learns to embed local information about the events and related actors in a news article through an auto-encoding framework, and to leverage this signal for document-level frame classification. Our experiments show that: our model outperforms previous models of frame prediction; we can further improve performance with unlabeled training data leveraging the semisupervised nature of our model; and the learnt event and actor embeddings intuitively corroborate the document-level predictions, providing a nuanced and interpretable article frame representation.

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“…We observe that the allies Mali and France are semantically more similar than enemies. Srivastava et al, 2016) and international relations extracted from news (O'Connor et al, 2013;Tan et al, 2017;Han et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

Classifying Dyads for Militarized Conflict Analysis

Stoehr¹,

Hennigen²,

Ahbab³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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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. 1

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No Permanent

Cited by 6 publications

References 27 publications

An Ordinal Latent Variable Model of Conflict Intensity

An Ordinal Latent Variable Model of Conflict Intensity

Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames

Classifying Dyads for Militarized Conflict Analysis

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