@inproceedings{schaefer-etal-2022-selecting,
title = "On Selecting Training Corpora for Cross-Domain Claim Detection",
author = "Schaefer, Robin and
Knaebel, Ren{\'e} and
Stede, Manfred",
editor = "Lapesa, Gabriella and
Schneider, Jodi and
Jo, Yohan and
Saha, Sougata",
booktitle = "Proceedings of the 9th Workshop on Argument Mining",
month = oct,
year = "2022",
address = "Online and in Gyeongju, Republic of Korea",
publisher = "International Conference on Computational Linguistics",
url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.argmining-1.17/",
pages = "181--186",
abstract = "Identifying claims in text is a crucial first step in argument mining. In this paper, we investigate factors for the composition of training corpora to improve cross-domain claim detection. To this end, we use four recent argumentation corpora annotated with claims and submit them to several experimental scenarios. Our results indicate that the {\textquotedblleft}ideal{\textquotedblright} composition of training corpora is characterized by a large corpus size, homogeneous claim proportions, and less formal text domains."
}
Markdown (Informal)
[On Selecting Training Corpora for Cross-Domain Claim Detection](https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.argmining-1.17/) (Schaefer et al., ArgMining 2022)
ACL