A Neural Approach for Detecting Morphological Analogies

Autor: Miguel Couceiro, Amandine Decker, Pierre-Alexandre Murena, Puthineath Lay, Esteban Marquer, Safa Alsaidi
Přispěvatelé: Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Semantic Analysis of Natural Language (SEMAGRAMME), Université de Lorraine (UL), Aalto University, Inria Project Lab 'Hybrid Approaches for Interpretable AI' (IPL HyAIAI), GRID5000, Institut des Sciences du Digital, Management et Cognition (IDMC), Aalto University School of Science and Technology [Aalto, Finland], Helsinki Institute for Information Technology (HIIT), Aalto University-University of Helsinki, European Project: 952215,TAILOR(2020), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Aalto University
Jazyk: angličtina
Rok vydání: 2021
Předmět:
FOS: Computer and information sciences
Computer Science - Machine Learning
Computer Science - Artificial Intelligence
Semantics (computer science)
Computer science
Analogy
Of the form
[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]
computer.software_genre
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
Machine Learning (cs.LG)
Data modeling
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
semantic analogy
analogy classification
[INFO]Computer Science [cs]
ComputingMilieux_MISCELLANEOUS
Computer Science - Computation and Language
morphological anaogy
Kolmogorov complexity
business.industry
Deep learning
Axiomatic system
deep learning
Artificial Intelligence (cs.AI)
Character (mathematics)
morphological analogy
Artificial intelligence
business
Computation and Language (cs.CL)
computer
Natural language processing
Zdroj: IEEE DSAA 2021-The 8th IEEE International Conference on Data Science and Advanced Analytics
IEEE DSAA 2021-The 8th IEEE International Conference on Data Science and Advanced Analytics, Oct 2021, Porto / Online, Portugal. IEEE DSAA 2021
The 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA)
The 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA), Oct 2021, Porto/Online, Portugal
DSAA
DSAA 2021-8th IEEE International Conference on Data Science and Advanced Analytics
DSAA 2021-8th IEEE International Conference on Data Science and Advanced Analytics, Oct 2021, Porto/Online, Portugal. pp.1-10
IEEE DSAA 2021-The 8th IEEE International Conference on Data Science and Advanced Analytics, Oct 2021, Porto / Online, Portugal., IEEE DSAA 2021
Popis: 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.
Comment: Submitted and accepted by the 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA)
Databáze: OpenAIRE
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