Classifying Papers from Different Computer Science Conferences

This paper analyzes what stylistic characteristics differentiate different styles of writing, and specifically types of different A-level computer science articles. To do so, we compared various full papers using stylistic feature sets and a supervised machine learning method. We report on the success of this approach in identifying papers from the last 6 years of the following three conferences: SIGIR, ACL, and AAMAS. This approach achieves high accuracy results of 95.86%, 97.04%, 93.22%, and 92.14% for the following four classification experiments: (1) SIGIR / ACL, (2) SIGIR / AAMAS, (3) ACL / AAMAS, and (4) SIGIR / ACL / AAMAS, respectively. The Part of Speech (PoS) and the Orthographic sets were superior to all others and have been found as key components in different types of writing.