Structured Argumentation Modeling and Extraction: Understanding the Semantics of Parliamentary Content

In information overload scenarios, the volume, structure and complexity of generated data represent a challenge that hinders the content comprehension. Aiming to overcome these dissuasive issues, the modeling and extraction of arguments in textual content has become a prominent topic in the information retrieval field. In this paper, we propose a new argumentation model, where different semantic components and their relationships are considered. Our proposal aims to enhance state of the art approaches, which limit their scope to identifying chunks of text as argumentative or not, leading to large amounts of texts left unanalyzed. The presented model, differently to domain-specific corpus methods, is designed to enable a generic, cross-lingual semantic annotation that promotes reusability. As a proof of concept, the model is exemplified in a case study for an e-government platform intended to annotate semantically, and provide information retrieval and filtering functionalities on content produced in the Spanish Parliament.

[1]  Serena Villata,et al.  DART: a Dataset of Arguments and their Relations on Twitter , 2016, LREC.

[2]  Karin Baier,et al.  The Uses Of Argument , 2016 .

[3]  Claire Cardie,et al.  An eRulemaking Corpus: Identifying Substantive Issues in Public Comments , 2008, LREC.

[4]  Moshe Azar,et al.  Argumentative Text as Rhetorical Structure: An Application of Rhetorical Structure Theory , 1999 .

[5]  Shalmoli Ghosh,et al.  Stance Detection in Web and Social Media: A Comparative Study , 2019, CLEF.

[6]  Claire Cardie,et al.  A Corpus of eRulemaking User Comments for Measuring Evaluability of Arguments , 2018, LREC.

[7]  Paolo Torroni,et al.  Argumentation Mining , 2016, ACM Trans. Internet Techn..

[8]  Danushka Bollegala,et al.  Is Something Better than Nothing? Automatically Predicting Stance-based Arguments Using Deep Learning and Small Labelled Dataset , 2018, NAACL-HLT.

[9]  Kalina Bontcheva,et al.  The evolution of argumentation mining: From models to social media and emerging tools , 2019, Inf. Process. Manag..

[10]  Manfred Stede,et al.  The Potsdam Commentary Corpus , 2004, ACL 2004.

[11]  Paolo Rosso,et al.  Stance Evolution and Twitter Interactions in an Italian Political Debate , 2018, NLDB.

[12]  Vlad Eidelman,et al.  Argument Identification in Public Comments from eRulemaking , 2019, ICAIL.

[13]  Junjie Lin,et al.  Multi-Target Stance Detection via a Dynamic Memory-Augmented Network , 2018, SIGIR.

[14]  William C. Mann,et al.  Discourse Structures for Text Generation , 1984, ACL.

[15]  Mihai Surdeanu,et al.  The Stanford CoreNLP Natural Language Processing Toolkit , 2014, ACL.

[16]  Paolo Rosso,et al.  Overview of the Task on Stance and Gender Detection in Tweets on Catalan Independence , 2017, IberEval@SEPLN.

[17]  Günther Öhlschläger.  Linguistische Überlegungen zu einer Theorie der Argumentation , 1979 .

[18]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[19]  Manfred Stede,et al.  From Argument Diagrams to Argumentation Mining in Texts: A Survey , 2013, Int. J. Cogn. Informatics Nat. Intell..

[20]  C. Reed,et al.  Mixed initiative argument in public deliberation , 2010 .

[21]  Quoc Viet Hung Nguyen,et al.  User Guidance for Efficient Fact Checking , 2019, Proc. VLDB Endow..

[22]  Alistair Knott,et al.  A data-driven methodology for motivating a set of coherence relations , 1996 .

[23]  Benno Stein,et al.  The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants , 2017, NAACL.

[24]  Gaku Morio,et al.  Annotating Online Civic Discussion Threads for Argument Mining , 2018, 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

[25]  Claire Cardie,et al.  Using Argumentative Structure to Interpret Debates in Online Deliberative Democracy and eRulemaking , 2017, ACM Trans. Internet Techn..

[26]  Syin Chan,et al.  Extracting Causal Knowledge from a Medical Database Using Graphical Patterns , 2000, ACL.

[27]  Francesca Toni,et al.  Combining Deep Learning and Argumentative Reasoning for the Analysis of Social Media Textual Content Using Small Data Sets , 2018, Computational Linguistics.

[28]  Elena Musi,et al.  Analyzing the Semantic Types of Claims and Premises in an Online Persuasive Forum , 2017, ArgMining@EMNLP.

[29]  Wolfgang Klein,et al.  Argumentation und Argument , 1980 .