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Multi-task Learning for Semantic Relations Discovery

Georgios Balikas 1 Gaël Dias 2 Massih-Reza Amini Houssam Akhmouch 3, 2
2 Equipe Hultech - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image et Instrumentation de Caen
Abstract : Identifying the semantic relations that hold between words is of crucial importance for reasoning purposes. Within this context, different methodolo-gies have been proposed that either exclusively focus on a single lexical relation (two-class problem) or learn specific classifiers capable of identifying multiple semantic relations (multi-class problem). In this paper, we propose another way to look at the problem that relies on the multi-task learning paradigm. Preliminary results based on simple learning strategies and state-of-the-art distributional feature representations show that concurrent learning can lead to improvements.
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Submitted on : Wednesday, November 14, 2018 - 9:03:14 AM
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  • HAL Id : hal-01921715, version 1


Georgios Balikas, Gaël Dias, Massih-Reza Amini, Houssam Akhmouch. Multi-task Learning for Semantic Relations Discovery. 21st International Conference on Knowledge Engineering and Knowledge Management (EKAW 2018), 2018, Nancy, France. ⟨hal-01921715⟩



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