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Homepage2Vec: Language-Agnostic Website Embedding and Classification ...
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Better than Average: Paired Evaluation of NLP systems ...
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Classifying Dyads for Militarized Conflict Analysis ...
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Classifying Dyads for Militarized Conflict Analysis
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
Abstract: 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.
URL: https://doi.org/10.3929/ethz-b-000518996
https://hdl.handle.net/20.500.11850/518996
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5
Cognitive Network Topology and Optimization of the Mental Lexicon ...
Burlacu, Adrian; West, Robert. - : Unpublished, 2021
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6
Linguistic effects on news headline success: Evidence from thousands of online field experiments (Registered Report Protocol)
In: PLoS One (2021)
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7
On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation ...
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On the limitations of cross-lingual encoders as exposed by reference-free machine translation evaluation
Zhao, Wei; Glavaš, Goran; Peyrard, Maxime. - : Association for Computational Linguistics, 2020
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Robust Cross-lingual Embeddings from Parallel Sentences ...
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Crosslingual Document Embedding as Reduced-Rank Ridge Regression ...
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Why the World Reads Wikipedia ...
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Why the World Reads Wikipedia ...
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Why the World Reads Wikipedia ...
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14
Causal Effects of Brevity on Style and Success in Social Media ...
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15
Message Distortion in Information Cascades
In: http://infoscience.epfl.ch/record/270657 (2019)
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Message Distortion in Information Cascades ...
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17
Reverse-Engineering Satire, or "Paper on Computational Humor Accepted despite Making Serious Advances"
In: http://infoscience.epfl.ch/record/271147 (2019)
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18
Why the World Reads Wikipedia: Beyond English Speakers
In: http://infoscience.epfl.ch/record/270302 (2019)
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19
Crosslingual Document Embedding as Reduced-Rank Ridge Regression
In: http://infoscience.epfl.ch/record/263893 (2019)
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20
Churn Intent Detection in Multilingual Chatbot Conversations and Social Media ...
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