PageRank” for Argument Relevance

Henning Wachsmuth, Benno Stein, Yamen Ajjour


Abstract
Future search engines are expected to deliver pro and con arguments in response to queries on controversial topics. While argument mining is now in the focus of research, the question of how to retrieve the relevant arguments remains open. This paper proposes a radical model to assess relevance objectively at web scale: the relevance of an argument’s conclusion is decided by what other arguments reuse it as a premise. We build an argument graph for this model that we analyze with a recursive weighting scheme, adapting key ideas of PageRank. In experiments on a large ground-truth argument graph, the resulting relevance scores correlate with human average judgments. We outline what natural language challenges must be faced at web scale in order to stepwise bring argument relevance to web search engines.
Anthology ID:
E17-1105
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1117–1127
Language:
URL:
https://aclanthology.org/E17-1105
DOI:
Bibkey:
Cite (ACL):
Henning Wachsmuth, Benno Stein, and Yamen Ajjour. 2017. “PageRank” for Argument Relevance. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1117–1127, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
“PageRank” for Argument Relevance (Wachsmuth et al., EACL 2017)
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PDF:
https://aclanthology.org/E17-1105.pdf