Symbolic, Distributed, and Distributional Representations for Natural Language Processing in the Era of Deep Learning : A Survey
Copyright © 2020 Ferrone and Zanzotto..
Natural language is inherently a discrete symbolic representation of human knowledge. Recent advances in machine learning (ML) and in natural language processing (NLP) seem to contradict the above intuition: discrete symbols are fading away, erased by vectors or tensors called distributed and distributional representations. However, there is a strict link between distributed/distributional representations and discrete symbols, being the first an approximation of the second. A clearer understanding of the strict link between distributed/distributional representations and symbols may certainly lead to radically new deep learning networks. In this paper we make a survey that aims to renew the link between symbolic representations and distributed/distributional representations. This is the right time to revitalize the area of interpreting how discrete symbols are represented inside neural networks.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:6 |
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| Enthalten in: |
Frontiers in robotics and AI - 6(2019) vom: 22., Seite 153 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ferrone, Lorenzo [VerfasserIn] |
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| Links: |
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| Themen: |
Compositional distributional semantic models |
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| Anmerkungen: |
Date Revised 29.01.2021 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/frobt.2019.00153 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Natural language is inherently a discrete symbolic representation of human knowledge. Recent advances in machine learning (ML) and in natural language processing (NLP) seem to contradict the above intuition: discrete symbols are fading away, erased by vectors or tensors called distributed and distributional representations. However, there is a strict link between distributed/distributional representations and discrete symbols, being the first an approximation of the second. A clearer understanding of the strict link between distributed/distributional representations and symbols may certainly lead to radically new deep learning networks. In this paper we make a survey that aims to renew the link between symbolic representations and distributed/distributional representations. This is the right time to revitalize the area of interpreting how discrete symbols are represented inside neural networks | ||
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