Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: From Frequency to Meaning: Vector Space Models of Semantics   Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field.

32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg