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.