Classifying papers from different computer science conferences

Yaakov HaCohen-Kerner, Avi Rosenfeld, Maor Tzidkani, Daniel Nisim Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


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.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - 9th International Conference, ADMA 2013, Proceedings
Number of pages13
EditionPART 1
StatePublished - 2013
Externally publishedYes
Event9th International Conference on Advanced Data Mining and Applications, ADMA 2013 - Hangzhou, China
Duration: 14 Dec 201316 Dec 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8346 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Conference on Advanced Data Mining and Applications, ADMA 2013


  • Classification and regression trees
  • Conference classification
  • Decision tree learning
  • Document classification
  • Feature sets
  • Text classification


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