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Argument Mining on Italian News Blogs

Abstract : The goal of argument mining is to extract structured information, namely the arguments and their relations, from un-structured text. In this paper, we propose an approach to argument relation prediction based on supervised learning of linguistic and semantic features of the text. We test our method on the CorEA corpus of user comments to online newspaper articles, evaluating our system's performances in assigning the correct relation, i.e., support or attack, to pairs of arguments. We obtain results consistently better than a sentiment analysis-based base-line (over two out three correctly classified pairs), and we observe that sentiment and lexical semantics are the most informative features with respect to the relation prediction task.
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Contributor : Valerio Basile Connect in order to contact the contributor
Submitted on : Monday, December 12, 2016 - 2:59:46 PM
Last modification on : Thursday, January 20, 2022 - 5:32:36 PM
Long-term archiving on: : Tuesday, March 28, 2017 - 12:55:17 AM


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  • HAL Id : hal-01414698, version 1



Pierpaolo Basile, Valerio Basile, Elena Cabrio, Serena Villata. Argument Mining on Italian News Blogs. Third Italian Conference on Computational Linguistics (CLiC-it 2016) & Fifth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2016), Dec 2016, Naples, Italy. ⟨hal-01414698⟩



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