Argument Mining on Italian News Blogs
Authors: Pierpaolo Basile, Valerio Basile, Elena Cabrio, Serena Villata (Università di Bari Aldo Moro, Université Cote d'Azur, 2016)
Topic: Lexical and Semantic Resources and Analysis

Abstract
The goal of argument mining is to extract structured information, namely the arguments and their relations, from unstructured 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 baseline (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.

Introduction
The argument mining AUTHOR research area has recently become very relevant in computational linguistics. Its main goal is the automated extraction of natural language arguments and their relations from generic textual corpora, with the final goal of providing machine-readable structured data for computational models of argument and reasoning engines. Two main stages have to be considered in the typical argument mining pipeline, from the unstructured natural language documents towards structured (possibly machine-readable) data: (i) argument extraction, i.e., to detect arguments within the input natural language texts, and (ii) relation extraction, i.e., to predict what are the relations holding between the arguments identified in the first stage. The relation prediction task is extremely complex, as it involves high-level knowledge representation and reasoning issues. The relations between the arguments may be of heterogeneous nature, like attack, support or entailment AUTHOR. The increasing amount of data available on the Web from heterogeneous sources, e.g., social network posts, forums, news blogs, and the specific form of language adopted there challenge argument mining methods, with the aim to support users to understand and interact with such a huge amount of information. In this paper, we address this issue by presenting an argument relation prediction approach for Italian. We test the method on the CorEA corpus AUTHOR of user comments to the news articles of an Italian newspaper, annotated with agreement (i.e., support) and disagreement (i.e., attack) relations. We extract argument-level features from the CorEA comment (i.e., argument) pairs, and we train our system to predict the support and attack relations.

Conclusion
In this paper, we have presented a supervised approach for argument relation prediction for Italian, mainly relying on features including semantics and sentiment. We tested such approach on the CorEA corpus, extracted from user comments to online news.Our experimental results are good, and foster future research in the direction of including semantics as well as sentiment analysis in the argument mining pipeline. It will be also interesting, as future work, to refine the model in order to consider the full sequence of interactions between arguments.