Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning Based Source Code Classification Using Syntax Oriented
  Features   As of today the programming language of the vast majority of the published
source code is manually specified or programmatically assigned based on the
sole file extension. In this paper we show that the source code programming
language identification task can be fully automated using machine learning
techniques. We first define the criteria that a production-level automatic
programming language identification solution should meet. Our criteria include
accuracy, programming language coverage, extensibility and performance. We then
describe our approach: How training files are preprocessed for extracting
features that mimic grammar productions, and then how these extracted grammar
productions are used for the training and testing of our classifier. We achieve
a 99 percent accuracy rate while classifying 29 of the most popular programming
languages with a Maximum Entropy classifier.

25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg