@inproceedings{aker-etal-2017-works,
title = "What works and what does not: Classifier and feature analysis for argument mining",
author = "Aker, Ahmet and
Sliwa, Alfred and
Ma, Yuan and
Lui, Ruishen and
Borad, Niravkumar and
Ziyaei, Seyedeh and
Ghobadi, Mina",
editor = "Habernal, Ivan and
Gurevych, Iryna and
Ashley, Kevin and
Cardie, Claire and
Green, Nancy and
Litman, Diane and
Petasis, Georgios and
Reed, Chris and
Slonim, Noam and
Walker, Vern",
booktitle = "Proceedings of the 4th Workshop on Argument Mining",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5112",
doi = "10.18653/v1/W17-5112",
pages = "91--96",
abstract = "This paper offers a comparative analysis of the performance of different supervised machine learning methods and feature sets on argument mining tasks. Specifically, we address the tasks of extracting argumentative segments from texts and predicting the structure between those segments. Eight classifiers and different combinations of six feature types reported in previous work are evaluated. The results indicate that overall best performing features are the structural ones. Although the performance of classifiers varies depending on the feature combinations and corpora used for training and testing, Random Forest seems to be among the best performing classifiers. These results build a basis for further development of argument mining techniques and can guide an implementation of argument mining into different applications such as argument based search.",
}
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%0 Conference Proceedings
%T What works and what does not: Classifier and feature analysis for argument mining
%A Aker, Ahmet
%A Sliwa, Alfred
%A Ma, Yuan
%A Lui, Ruishen
%A Borad, Niravkumar
%A Ziyaei, Seyedeh
%A Ghobadi, Mina
%Y Habernal, Ivan
%Y Gurevych, Iryna
%Y Ashley, Kevin
%Y Cardie, Claire
%Y Green, Nancy
%Y Litman, Diane
%Y Petasis, Georgios
%Y Reed, Chris
%Y Slonim, Noam
%Y Walker, Vern
%S Proceedings of the 4th Workshop on Argument Mining
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F aker-etal-2017-works
%X This paper offers a comparative analysis of the performance of different supervised machine learning methods and feature sets on argument mining tasks. Specifically, we address the tasks of extracting argumentative segments from texts and predicting the structure between those segments. Eight classifiers and different combinations of six feature types reported in previous work are evaluated. The results indicate that overall best performing features are the structural ones. Although the performance of classifiers varies depending on the feature combinations and corpora used for training and testing, Random Forest seems to be among the best performing classifiers. These results build a basis for further development of argument mining techniques and can guide an implementation of argument mining into different applications such as argument based search.
%R 10.18653/v1/W17-5112
%U https://aclanthology.org/W17-5112
%U https://doi.org/10.18653/v1/W17-5112
%P 91-96
Markdown (Informal)
[What works and what does not: Classifier and feature analysis for argument mining](https://aclanthology.org/W17-5112) (Aker et al., ArgMining 2017)
ACL