Using Argumentative Structure to Grade Persuasive Essays

被引:0
作者
Stiegelmayr, Andreas [1 ]
Mieskes, Margot [1 ]
机构
[1] Univ Appl Sci, Darmstadt, Germany
来源
LANGUAGE TECHNOLOGIES FOR THE CHALLENGES OF THE DIGITAL AGE, GSCL 2017 | 2018年 / 10713卷
关键词
D O I
10.1007/978-3-319-73706-5_26
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this work we analyse a set of persuasive essays, which were marked and graded with respect to their overall quality. Additionally, we performed a small-scale machine learning experiment incorporating features from the argumentative analysis in order to automatically classify good and bad essays on a four-point scale. Our results indicate that bad essays suffer from more than just incomplete argument structures, which is already visible using simple surface features. We show that good essays distinguish themselves in terms of the amount of argumentative elements (such as major claims, premises, etc.) they use. The results, which have been obtained using a small corpus of essays in German, indicate that information about the argumentative structure of a text is helpful in distinguishing good and bad essays.
引用
收藏
页码:301 / 308
页数:8
相关论文
共 11 条
[1]  
Dong F., 2016, P 2016 C EMP METH NA, P1072, DOI DOI 10.18653/V1/D16-1115
[2]  
Faulkner A., 2014, THESIS
[3]  
Ghosh D, 2016, PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2016), VOL 2, P549
[4]   Argumentation Mining in User-Generated Web Discourse [J].
Habernal, Ivan ;
Gurevych, Iryna .
COMPUTATIONAL LINGUISTICS, 2017, 43 (01) :125-179
[5]  
Houy C, 2013, 7 IEEE INT C DIG EC
[6]  
Kluge Roland, 2014, SEARCHING ARGUMENTS
[7]   MEASUREMENT OF OBSERVER AGREEMENT FOR CATEGORICAL DATA [J].
LANDIS, JR ;
KOCH, GG .
BIOMETRICS, 1977, 33 (01) :159-174
[8]  
Meyer CM, 2014, P 25 INT C COMP LING, P105
[9]  
Peldszus Andreas, 2013, P 7 LINGUISTIC ANNOT, P196
[10]  
Stab C., 2014, P EMNLP, P46