A Neural Approach for Detecting Morphological Analogies

Safa Alsaidi, Amandine Decker, Puthineath Lay, Esteban Marquer, Pierre-Alexandre Murena, Miguel Couceiro

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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.
Original languageEnglish
Title of host publication2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)
PublisherIEEE
ISBN (Print)9781665421003
DOIs
Publication statusPublished - 6 Oct 2021
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Data Science and Advanced Analytics - Porto, Portugal
Duration: 6 Oct 20219 Oct 2021
Conference number: 8

Conference

ConferenceIEEE International Conference on Data Science and Advanced Analytics
Abbreviated titleDSAA
Country/TerritoryPortugal
CityPorto
Period06/10/202109/10/2021

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