Siganalogies - morphological analogies from Sigmorphon 2016 and 2019

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Marquer, Esteban; Couceiro, Miguel; Safa Alsaidi; Amandine Decker, 2022, "Siganalogies - morphological analogies from Sigmorphon 2016 and 2019", https://doi.org/10.12763/MLCFIE, Université de Lorraine, V1

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Description	The siganalogies dataset contains morphological analogies built upon Sigmorphon 2016 and Sigmorphon 2019 in PyTorch. An analogical proportion is defined as a 4-ary relation written A:B::C:D and which reads "A is to B as C is to D". In this dataset, we manipulate morphological analogies, i.e., on analogies involving character strings, where the transformations between the objects correspond to morphological transformations of words (e.g., conjugation or declension). In our dataset, A, B, C, and D are words. An example in English would be "dog : dogs :: cat : cats". The dataset contains: (i) a copy of Sigmorphon 2019 and Sigmorphon 2016 extended with Japanese data, (ii) serialized objects, one for each language, containing the indices of the analogies and other relevant data, and (iii) the code necessary to manipulate the dataset and serialized data.
Subject	Arts and Humanities; Computer and Information Science
Keyword	Morphology (linguistics), Multilingualism, Analogy
Related Publication	Alsaidi, Safa & Decker, Amandine & Lay, Puthineath & Marquer, Esteban & Murena, Pierre-Alexandre & Couceiro, Miguel. (2021). On the Transferability of Neural Models of Morphological Analogies. AIMLAI 2021 - workshop on Advances in Interpretable Machine Learning and Artificial Intelligence null: hal-03313591
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	README.md sigmorphon2016/Markdown Text - 17 B ???published??? Mar 16, 2022 18 Downloads MD5: f98081475b7dfd722890f72faf606a24	Access File File Access ???public??? Download Options Markdown Text Download Metadata Data File Citation EndNote XML RIS BibTeX
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Dataset Persistent ID	doi:10.12763/MLCFIE
Publication Date	2022-03-16
Title	Siganalogies - morphological analogies from Sigmorphon 2016 and 2019
Alternative URL	https://github.com/EMarquer/siganalogies
Author	Marquer, Esteban (LORIA) - ORCID: 0000-0003-2315-7732 Couceiro, Miguel (LORIA) - ORCID: 0000-0003-2316-7623 Safa Alsaidi (Université de Lorraine) - ORCID: 0000-0002-4132-1068 Amandine Decker (Université de Lorraine) - ORCID: 0000-0001-6773-9983
Contact	Use email button above to contact. Marquer, Esteban (LORIA) Couceiro, Miguel (LORIA)
Description	The siganalogies dataset contains morphological analogies built upon Sigmorphon 2016 and Sigmorphon 2019 in PyTorch. An analogical proportion is defined as a 4-ary relation written A:B::C:D and which reads "A is to B as C is to D". In this dataset, we manipulate morphological analogies, i.e., on analogies involving character strings, where the transformations between the objects correspond to morphological transformations of words (e.g., conjugation or declension). In our dataset, A, B, C, and D are words. An example in English would be "dog : dogs :: cat : cats". The dataset contains: (i) a copy of Sigmorphon 2019 and Sigmorphon 2016 extended with Japanese data, (ii) serialized objects, one for each language, containing the indices of the analogies and other relevant data, and (iii) the code necessary to manipulate the dataset and serialized data.
Subject	Arts and Humanities; Computer and Information Science
Keyword	Morphology (linguistics) (UNESCO) http://vocabularies.unesco.org/browser/thesaurus/en/ Multilingualism (UNESCO) http://vocabularies.unesco.org/browser/thesaurus/en/ Analogy
Related Publication	Alsaidi, Safa & Decker, Amandine & Lay, Puthineath & Marquer, Esteban & Murena, Pierre-Alexandre & Couceiro, Miguel. (2021). On the Transferability of Neural Models of Morphological Analogies. AIMLAI 2021 - workshop on Advances in Interpretable Machine Learning and Artificial Intelligence hal-03313591 https://hal.univ-lorraine.fr/LORIA-NLPKD/hal-03313591v1 Alsaidi, Safa and Decker, Amandine and Lay, Puthineath and Marquer, Esteban and Murena, Pierre-Alexandre and Couceiro, Miguel. (2012). A Neural Approach for Detecting Morphological Analogies. DSAA 2021 - 8th IEEE International Conference on Data Science and Advanced Analytics, p. 1-10 hal-03313556 https://hal.inria.fr/hal-03313556
Language	Albanian; Arabic; Armenian; Bashkir; Basque; Belarusian; Bengali, Bangla; Bulgarian; Czech; Danish; Dutch; English; Estonian; Finnish; French; Georgian; German; Greek (modern); Hebrew (modern); Hindi; Hungarian; Irish; Italian; Japanese; Kannada; Latin; Maltese; Navajo, Navaho; Persian (Farsi); Polish; Portuguese; Romanian; Russian; Sanskrit (Saṁskṛta); Slovak; Slovene; Spanish, Castilian; Swahili; Turkish; Urdu; Uzbek; Welsh; Zulu
Contributor	Project Leader : Miguel Couceiro Project Leader : Esteban Marquer Project Member : Amandine Decker Project Member : Safa Alsaidi Project Member : Putineath Lay Project Member : Pierre-Alexandre Murena
Depositor	Marquer, Esteban
Deposit Date	2022-03-14
Kind of Data	Software; Text
Other Kind of Data	Pytorch serialized objects; Python code
Software	Python, Version: 3.8 PyTorch, Version: 1.10
Related Material	https://github.com/EMarquer/siganalogies/blob/main/siganalogies_description.pdf
Related Datasets	https://github.com/ryancotterell/sigmorphon2016; https://github.com/sigmorphon/2019

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