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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 On Homophony and Rényi Entropy ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cotterell, Ryan; Meister, Clara. - : Underline Science Inc., 2021 BASE Show details 2 Revisiting the Uniform Information Density Hypothesis ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cotterell, Ryan; Haller, Patrick. - : Underline Science Inc., 2021 BASE Show details 3 Conditional Poisson Stochastic Beams ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Amini, Afra; Cotterell, Ryan. - : Underline Science Inc., 2021 BASE Show details 4 Differentiable Subset Pruning of Transformer Heads ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cotterell, Ryan; Li, Jiaoda. - : Underline Science Inc., 2021 BASE Show details 5 A Bayesian Framework for Information-Theoretic Probing ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cotterell, Ryan; Pimentel, Tiago. - : Underline Science Inc., 2021 BASE Show details 6 Classifying Dyads for Militarized Conflict Analysis ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ahbab, Samin; Cotterell, Ryan; Stoehr, Niklas; Torroba Hennigen, Lucas; West, Robert. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.613/ 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 ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Sentiment Analysis URL: https://dx.doi.org/10.48448/egna-z603 https://underline.io/lecture/37327-classifying-dyads-for-militarized-conflict-analysis BASE Hide details 7 A surprisal--duration trade-off across and within the world's languages ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Blasi, Damián; Cotterell, Ryan. - : Underline Science Inc., 2021 BASE Show details 8 On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Blasi, Damián; Cotterell, Ryan. - : Underline Science Inc., 2021 BASE Show details

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