Argumentation Mining: Exploiting Multiple Sources and Background Knowledge (original) (raw)
Argumentation Mining: Techniques and Emerging Trends
Journal of University of Shanghai for Science and Technology, 2021
By Argument we mean persuasion of a reason or reasons in support of a claim or evidence. In Artificial Intelligence computational argumentation is the field dealing with computational logic upon which many models of argumentation have been suggested. The goal of Argumentation Mining is to automatically extract structured arguments from the unstructured text. It has the potential of extracting information from web and social media, making it one of the most sought after research area. Some recent advances in computational logic and Machine Learning methods do provide a new insight to the applications for policy making, economic sciences, legal, medical and information technology. Different models have been proposed for argumentation mining with different machine learning methods applied on the argumentation frameworks proposed for this particular mining task. In this survey article we will review the existing systems and applications and will cover the three categories of argumentati...
The evolution of argumentation mining: From models to social media and emerging tools
Information Processing & Management
Argumentation mining is a rising subject in the computational linguistics domain focusing on extracting structured arguments from natural text, often from unstructured or noisy text. The initial approaches on modeling arguments was aiming to identify a flawless argument on specific fields (Law, Scientific Papers) serving specific needs (completeness, effectiveness). With the emerge of Web 2.0 and the explosion in the use of social media both the diffusion of the data and the argument structure have changed. In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media. We review, compare, and classify existing approaches, techniques and tools, identifying the positive outcome of combining tasks and features, and eventually propose a conceptual architecture framework. The proposed theoretical framework is an argumentation mining scheme able to identify the distinct sub-tasks and capture the needs of social media text, revealing the need for adopting more flexible and extensible frameworks.
Some Facets of Argument Mining for Opinion Analysis
2012
In this paper, we present some foundational elements related to argument extraction in opinion texts with the objective to further analyse and synthetise user preferences and value systems as they emerge in such texts. We show that (1) within the context of opinionated expressions, a number of evaluative expressions with a ’heavy’ semantic load receive an argumentative interpretation and (2) that the association of an evaluative expression with a discourse structure such as an elaboration, an illustration, or a reformulation must also be interpreted as an argument. We develop a conceptual semantics of these relations and show how they are analyzed using the Dislog programming language on the platform, dedicated to discourse analysis.
Knowledge-Driven Argument Mining: what we learn from corpus analysis
2016
Given a controversial issue, argument mining from texts in natural language is extremely challenging: besides linguistic aspects, domain knowledge is often required together with appropriate forms of inferences to identify arguments. Via the the analysis of various corpora, this contribution explores the types of knowledge that are required to develop an efficient argument mining system.
Combining Argument Mining Techniques
Proceedings of the 2nd Workshop on Argumentation Mining, 2015
In this paper, we look at three different methods of extracting the argumentative structure from a piece of natural language text. These methods cover linguistic features, changes in the topic being discussed and a supervised machine learning approach to identify the components of argumentation schemes, patterns of human reasoning which have been detailed extensively in philosophy and psychology. For each of these approaches we achieve results comparable to those previously reported, whilst at the same time achieving a more detailed argument structure. Finally, we use the results from these individual techniques to apply them in combination, further improving the argument structure identification.
Automatic argumentative analysis for interaction mining
Argument & Computation, 2011
Interaction mining is about discovering and extracting insightful information from digital conversations, namely those human–human information exchanges mediated by digital network technology. We present in this article a computational model of natural arguments and its implementation for the automatic argumentative analysis of digital conversations, which allows us to produce relevant information to build interaction business analytics applications overcoming the limitations of standard text mining and information retrieval technology. Applications include advanced visualisations and abstractive summaries.
An Argument-Ontology for a Response-Centered Approach to Argumentation Mining
2016
An interesting practical problem for argumentation mining is the detection of argument in a specific social or cultural context. Communicative-rhetorical actions may look like argument with overt or contentious linguistic markers (e.g., ‘well’, ‘but’, ‘that’s stupid’, ‘I disagree’) but may not function as argument. Communicative-rhetorical actions may include or point to reasons but without any obvious argumentative function (e.g., explaining, clarifying). Moreover, reasoning to resolve a difference can happen implicitly among participants with only traces of the jointly owned reasoning evident in the language use. The challenge becomes how to mine in a way that excludes language that only appears to be argumentative, while including the nonobvious uses of language for argument. A response-centered approach for the context sensitive discovery and classification of argument in argumentation mining is outlined here. It is built around a conceptualization of argument as the use of lang...
Corpus Wide Argument Mining—A Working Solution
Proceedings of the AAAI Conference on Artificial Intelligence
One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively small number of relevant documents as the initial source for such content. This line of research yielded moderate success, which is of limited use in a real-world system. Furthermore, for such a system to yield a comprehensive set of relevant arguments, over a wide range of topics, it requires leveraging a large and diverse corpus in an appropriate manner. Here we present a first end-to-end high-precision, corpus-wide argument mining system. This is made possible by combining sentence-level queries over an appropriate indexing of a very large corpus of newspaper articles, with an iterative annotation scheme. This scheme addresses the inherent label bias in the data and pinpoints the regions of the sample space whose manual labeling is required to obtain high-precision among top-ranked candidates.
Towards Argument Mining from Relational DataBase
2008
Argumentation theory is considered an interdisciplinary research area. Its techniques and results have found a wide range of applications in both theoretical and practical branches of artificial intelligence, education, and computer science. Most of the work done in argumentation use the on-line textual data (i.e. unstructured or semi-structured) which is intractable to be processed. This paper reports a novel approach to build a Relational Argument DataBase (RADB) with managing tools for argument mining, the design of the RADB depends on the Argumentation Interchange Format Ontology(AIF) using "Walton Theory". The proposed structure aims to: (i) summon and provide a myriad of arguments at the user's fingertips, (ii) retrieve the most relevant results to the subject of search, (iii) support the fast interaction between the different mining techniques and the existing arguments, and (iv) facilitate the interoperability among various agents/humans.
Decompositional Argument Mining: A General Purpose Approach for Argument Graph Construction
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
This work presents an approach decomposing propositions into four functional components and identify the patterns linking those components to determine argument structure. The entities addressed by a proposition are target concepts and the features selected to make a point about the target concepts are aspects. A line of reasoning is followed by providing evidence for the points made about the target concepts via aspects. Opinions on target concepts and opinions on aspects are used to support or attack the ideas expressed by target concepts and aspects. The relations between aspects, target concepts, opinions on target concepts and aspects are used to infer the argument relations. Propositions are connected iteratively to form a graph structure. The approach is generic in that it is not tuned for a specific corpus and evaluated on three different corpora from the literature: AAEC, AMT, US2016G1tv and achieved an F score of 0.79, 0.77 and 0.64, respectively.
Dagstuhl Reports, 2016
This report documents the program and the outcomes of Dagstuhl Seminar 16161 "Natural Language Argumentation: Mining, Processing, and Reasoning over Textual Arguments", 17--22 April 2016. The seminar brought together leading researchers from computational linguistics, argumentation theory and cognitive psychology communities to discuss the obtained results and the future challenges of the recently born Argument Mining research area. 40 participants from 14 different countries took part in 7 sessions that included 30 talks, two tutorials, and a hands-on unshared task.
Towards Argument Mining from Dialogue
2014
Argument mining has started to yield early results in automatic analysis of text to produce representations of reason-conclusion structures. This paper addresses for the first time the question of automatically extracting such structures from dialogical settings of argument. More specifically, we introduce theoretical foundations for dialogical argument mining as well as show the initial implementation in a software for dialogue processing, and the application in corpus analysis. We combine analysis of illocutionary structure with structured argumentation frameworks as our scaffolding, and apply a combination of statistical and grammatically based analytical techniques.
Argument Mining Using Highly Structured Argument Repertoire
Argumentation theory is considered an interdisciplinary research area. Its tech- niques and results have found a wide range of applications in both theoretical and practical branches of artiflcial intelligence, education, and computer science. Most of the work done in argumentation use the on-line textual data (i.e. unstructured or semi-structured) which is intractable to be processed. This paper reports a novel approach to build a Relational Argu- ment DataBase (RADB) with managing tools for argument mining, the design of the RADB depends on the Argumentation Interchange Format Ontology(AIF) using "Walton Theory". The proposed structure aims to: (i) summon and provide a myriad of arguments at the user's flngertips, (ii) retrieve the most relevant results to the subject of search, (iii) support the fast interaction between the difierent mining techniques and the existing arguments, and (iv) facilitate the interoperability among various agents/humans.
Combining argumentation and aspect-based opinion mining: The SMACk system1
AI Communications, 2018
The extraction of the relevant and debated opinions from online social media and commercial websites is an emerging task in the opinion mining research field. Its growing relevance is mainly due to the impact of exploiting such techniques in different application domains from social science analysis to personal advertising. In this paper, we present SMACk, our opinion summary system built on top of an argumentation framework with the aim to exchange, communicate and resolve possibly conflicting viewpoints. SMACk allows the user to extract debated opinions from a set of documents containing user-generated content from online commercial websites, and to automatically identify the mostly debated positive aspects of the issue of the debate, as well as the mostly debated negative ones. The key advantage of such a framework is the combination of different methods, i.e., formal argumentation theory and natural language processing, to support users in making more informed decisions, e.g., in the context of online purchases.
The CASS Technique for Evaluating the Performance of Argument Mining
Proceedings of the Third Workshop on Argument Mining (ArgMining2016), 2016
Argument mining integrates many distinct computational linguistics tasks, and as a result, reporting agreement between annotators or between automated output and gold standard is particularly challenging. More worrying for the field, agreement and performance are also reported in a wide variety of different ways, making comparison between approaches difficult. To solve this problem, we propose the CASS technique for combining metrics covering different parts of the argument mining task. CASS delivers a justified method of integrating results yielding confusion matrices from which CASS-κ and CASS-F1 scores can be calculated.
Analysis of Discussions in Twitter with an Argumentation Tool
2016
The analysis of opinions on general and specialized social networks, has recently received a lot of attention on many application fields. For example, there is a vivid interest in the analysis of opinions of tourists about destinations and facilities, aimed at getting insight on tourist behavior and preferences for improvement and investment policy planning (McCarthy and Stock 2010; Villatoro et al. 2013; Williams et al. 2015), and similar efforts are being done on marketing (Burton and Soboleva 2011), customer engagement (Zhang, Jansen, and Chowdhury 2011), and related fields (Jansen et al. 2009; Chu and Kim 2011). Less numerous are the contributions centered around analyzing, not individual opinions, but debates and conversations where the structural relations between opinions are a key component to be able to pinpoint the accepted, or winning, opinions in a discussion. In this line, key contributions are the works of Atkinson et al. about using argumentation for tools to support ...
Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining
arXiv (Cornell University), 2018
Internet users generate content at unprecedented rates. Building intelligent systems capable of discriminating useful content within this ocean of information is thus becoming a urgent need. In this paper, we aim to predict the usefulness of Amazon reviews, and to do this we exploit features coming from an offthe-shelf argumentation mining system. We argue that the usefulness of a review, in fact, is strictly related to its argumentative content, whereas the use of an already trained system avoids the costly need of relabeling a novel dataset. Results obtained on a large publicly available corpus support this hypothesis.
2016
Rapid growth in the area of argument mining has resulted in an ever increasing volume of analysed argument data. Being able to store information about arguments people make in favour or against different opinions, decisions and actions is a highly valuable resource, yet extremely challenging for sense-making. How, for example, can an analyst quickly check whether in a corpus of citizen dialogue people tend to rather agree or disagree with new policies proposed by the department of transportation; how can she get an insight into the interactions typical of this specific dialogical context; how can the general public easily see which presidential candidate is currently winning the debate by being able to successfully defend his arguments? In this paper, we propose Argument Analytics – a suite of techniques which provide interpretation of, and insight into, large-scale argument data for both specialist and general audiences.