Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Symbolic, Distributed and Distributional Representations for Natural
  Language Processing in the Era of Deep Learning: a Survey   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.

16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th