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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 – 4 of 4 1 A Neural Approach for Detecting Morphological Analogies Alsaidi, Safa; Decker, Amandine; Lay, Puthineath... In: The 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA) ; https://hal.inria.fr/hal-03313556 ; The 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA), Oct 2021, Porto/Online, Portugal (2021) BASE Show details 2 Tackling Morphological Analogies Using Deep Learning -- Extended Version Alsaidi, Safa; Decker, Amandine; Marquer, Esteban; Murena, Pierre-Alexandre; Couceiro, Miguel In: https://hal.inria.fr/hal-03425776 ; 2021 (2021) Abstract: Analogical proportions are statements of the form "A is to B as C is to D". They constitute an inference tool that provides a logical framework to address learning, transfer, and explainability concerns and that finds useful applications in artificial intelligence and natural language processing. In this paper, we address two problems, namely, analogy detection and resolution in morphology. Multiple symbolic approaches tackle the problem of analogies in morphology and achieve competitive performance. We show that it is possible to use a data-driven strategy to outperform those models. We propose an approach using deep learning to detect and solve morphological analogies. It encodes structural properties of analogical proportions and relies on a specifically designed embedding model capturing morphological characteristics of words. We demonstrate our model's competitive performance on analogy detection and resolution over multiple languages. We provide an empirical study to analyze the impact of balancing training data and evaluate the robustness of our approach to input perturbation. Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]; Analogy detection; Analogy solving; Classification ·Regression; Morphological word embeddings; Neural networks URL: https://hal.inria.fr/hal-03425776 BASE Hide details 3 On the Transferability of Neural Models of Morphological Analogies Alsaidi, Safa; Decker, Amandine; Lay, Puthineath... In: AIMLAI, ECML PKDD 2021: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases ; https://hal.inria.fr/hal-03313591 ; AIMLAI, ECML PKDD 2021: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2021, Bilbao/Virtual, Spain ; https://2021.ecmlpkdd.org/ (2021) BASE Show details 4 On the Transferability of Neural Models of Morphological Analogies ... Alsaidi, Safa; Decker, Amandine; Lay, Puthineath. - : arXiv, 2021 BASE Show details

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