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

## TL;DR

This survey explores the relationship between symbolic and distributed representations in NLP, emphasizing their connection and potential for advancing deep learning models by revitalizing symbolic interpretation within neural networks.

## Contribution

It provides a comprehensive review of the link between symbolic and distributed representations, proposing renewed understanding to enhance deep learning approaches.

## Key findings

- Distributed representations approximate symbolic ones
- Understanding this link can lead to new neural network architectures
- Revitalizing symbolic interpretation in neural networks is timely

## Abstract

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.