On the Transferability of Neural Models of Morphological Analogies
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A4 Artikkeli konferenssijulkaisussa
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2021-09
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en
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Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II, Communications in Computer and Information Science ; Volume 1524
Abstract
Analogical proportions are statements expressed in the form "A is to B as C is to D" and are used for several reasoning and classification tasks in artificial intelligence and natural language processing (NLP). In this paper, we focus on morphological tasks and we propose a deep learning approach to detect morphological analogies. We present an empirical study to see how our framework transfers across languages, and that highlights interesting similarities and differences between these languages. In view of these results, we also discuss the possibility of building a multilingual morphological model.Description
| openaire: EC/H2020/952215/EU//TAILOR
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Alsaidi, S, Decker, A, Lay, P, Marquer, E, Murena, P-A & Couceiro, M 2021, On the Transferability of Neural Models of Morphological Analogies . in Machine Learning and Principles and Practice of Knowledge Discovery in Databases : International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II . Communications in Computer and Information Science, vol. 1524, Springer, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Virtual, Online, 13/09/2021 . https://doi.org/10.1007/978-3-030-93736-2_7