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A Neural Approach for Detecting Morphological Analogies

Abstract : Analogical proportions are statements of the form "A is to B as C is to D" that are used for several reasoning and classification tasks in artificial intelligence and natural language processing (NLP). For instance, there are analogy based approaches to semantics as well as to morphology. In fact, symbolic approaches were developed to solve or to detect analogies between character strings, e.g., the axiomatic approach as well as that based on Kolmogorov complexity. In this paper, we propose a deep learning approach to detect morphological analogies, for instance, with reinflexion or conjugation. We present empirical results that show that our framework is competitive with the above-mentioned state of the art symbolic approaches. We also explore empirically its transferability capacity across languages, which highlights interesting similarities between them.
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Submitted on : Wednesday, August 4, 2021 - 11:42:43 AM
Last modification on : Thursday, August 4, 2022 - 5:18:49 PM
Long-term archiving on: : Friday, November 5, 2021 - 6:33:08 PM

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  • HAL Id : hal-03313556, version 1

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Safa Alsaidi, Amandine Decker, Puthineath Lay, Esteban Marquer, Pierre-Alexandre Murena, et al.. A Neural Approach for Detecting Morphological Analogies. DSAA 2021 - 8th IEEE International Conference on Data Science and Advanced Analytics, Oct 2021, Porto/Online, Portugal. pp.1-10. ⟨hal-03313556⟩

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