Tracing armed conflicts with diachronic word embedding models

Kutuzov, Andrey; Velldal, Erik; Øvrelid, Lilja

doi:10.18653/v1/w17-2705

Cited by 50 publications

(61 citation statements)

References 8 publications

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“…These tasks are critical challenges that are relevant to a range of NLP fields, including the study of historical semantic change. The successful outcome of semantic change detection is relevant to any diachronic textual analysis, including machine translation or normalization of historical texts (Tjong Kim Sang et al, 2017), the detection of cultural semantic shifts (Kutuzov et al, 2017) or applications in digital humanities (Tahmasebi and Risse, 2017a). However, currently, the best-performing models (Hamilton et al, 2016b;Kulkarni et al, 2015;Schlechtweg et al, 2019) require a complex alignment procedure and have been shown to suffer from biases (Dubossarsky et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change

Dubossarsky¹,

Hengchen²,

Tahmasebi³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

105

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State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for lexical semantic change and show that, by avoiding alignment, it is less affected by this noise. We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset. We introduce a principled way to simulate lexical semantic change and systematically control for possible biases. * The order has been randomly determined and all authors contributed equally to this work.

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Section: Introductionmentioning

confidence: 99%

Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change

Dubossarsky¹,

Hengchen²,

Tahmasebi³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

105

View full text Add to dashboard Cite

show abstract

“…Scalable ground census using natural language processing also works well when a curated target-specific learning [16]. However, such approaches do not integrate the growing data collection (e.g.…”

Section: Literature Reviewmentioning

confidence: 99%

Gang confrontation: The case of Medellin (Colombia)

Botero¹,

Guo²,

Mosquera³

et al. 2019

PLoS ONE

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Protracted conflict is one of the largest human challenges that have persistently undermined economic and social progress. In recent years, there has been increased emphasis on using statistical and physical science models to better understand both the universal patterns and the underlying mechanics of conflict. Whilst macroscopic power-law fractal patterns have been shown for death-toll in wars and self-excitation models have been shown for roadside ambush attacks, very few works deal with the challenge of complex dynamics between gangs at the intra-city scale. Here, based on contributions to the historical memory of the conflict in Colombia, Medellin’s gang-confrontation-network is presented. It is shown that socio-economic and violence indexes are moderate to highly correlated to the structure of the network. Specifically, the death-toll of conflict is strongly influenced by the leading eigenvalues of the gangs’ conflict adjacency matrix, which serves a proxy for unstable self-excitation from revenge attacks. The distribution of links based on the geographic distance between gangs in confrontation leads to the confirmation that territorial control is a main catalyst of violence and retaliation among gangs. As a first attempt to explore the time evolution of the confrontation network, the Boltzmann-Lotka-Volterra (BLV) dynamic interaction network analysis is applied to quantify the spatial embeddedness of the dynamic relationship between conflicting gangs in Medellin. However, the non-stationary character of the violence in Medellin during the observation period restricts the application of the BLV model and results suggest that more involved and comprehensive models are needed to described the dynamics of Medellin’s armed conflict.

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“…It encodes conflicts, where at least one party is the government of a state. The Armed Conflict Dataset is widely used in statistical and macro-level conflict research; however, it was adapted and introduced to the NLP field only recently, starting with (Kutuzov et al, 2017). Whereas that work was focused on detecting the onset/endpoint of armed conflicts, the current paper further extends on this by using the dataset to evaluate the detection of changes in the semantic relation holding between participants of armed conflicts and their locations.…”

Section: Gold Standard Data On Armed Conflictsmentioning

confidence: 99%

Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants

Kutuzov

Velldal

Øvrelid

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Self Cite

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This paper deals with using word embedding models to trace the temporal dynamics of semantic relations between pairs of words. The set-up is similar to the well-known analogies task, but expanded with a time dimension. To this end, we apply incremental updating of the models with new training texts, including incremental vocabulary expansion, coupled with learned transformation matrices that let us map between members of the relation. The proposed approach is evaluated on the task of predicting insurgent armed groups based on geographical locations. The gold standard data for the time span 1994-2010 is extracted from the UCDP Armed Conflicts dataset. The results show that the method is feasible and outperforms the baselines, but also that important work still remains to be done.

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Tracing armed conflicts with diachronic word embedding models

Cited by 50 publications

References 8 publications

Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change

Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change

Gang confrontation: The case of Medellin (Colombia)

Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants

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