TY - GEN
T1 - Classifying papers from different computer science conferences
AU - HaCohen-Kerner, Yaakov
AU - Rosenfeld, Avi
AU - Tzidkani, Maor
AU - Cohen, Daniel Nisim
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Classification and regression trees
KW - Conference classification
KW - Decision tree learning
KW - Document classification
KW - Feature sets
KW - Text classification
UR - http://www.scopus.com/inward/record.url?scp=84893056017&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-53914-5_45
DO - 10.1007/978-3-642-53914-5_45
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AN - SCOPUS:84893056017
SN - 9783642539138
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 529
EP - 541
BT - Advanced Data Mining and Applications - 9th International Conference, ADMA 2013, Proceedings
T2 - 9th International Conference on Advanced Data Mining and Applications, ADMA 2013
Y2 - 14 December 2013 through 16 December 2013
ER -