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Symbolic,Distributed and Distributional Representations for Natural Language Processing in the Era of Deep Learning:a Survey - 2017

Research Area:  Machine Learning


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.

Author(s) Name:  Lorenzo Ferrone, Fabio Massimo Zanzotto

Journal name:  Computer Science

Conferrence name:  

Publisher name:  arXiv:1702.00764

DOI:  10.3389/frobt.2019.00153

Volume Information: