A minimum message length approach for argument interpretation

2016

We propose a new approach for extracting argument structure from natural language texts that contain an underlying argument. Our approach comprises of two phases: Score Assignment and Structure Prediction. The Score Assignment phase trains models to classify relations between argument units (Support, Attack or Neutral). To that end, different training strategies have been explored. We identify different linguistic and lexical features for training the classifiers. Through ablation study, we observe that our novel use of word-embedding features is most effective for this task. The Structure Prediction phase makes use of the scores from the Score Assignment phase to arrive at the optimal structure. We perform experiments on three argumentation datasets, namely, AraucariaDB, Debatepedia and Wikipedia. We also propose two baselines and observe that the proposed approach outperforms baseline systems for the final task of Structure Prediction.

International Journal of Cognitive Informatics and Natural Intelligence, 6(3), 2012, 33-61.

This paper surveys the state-of-the-art of argumentation schemes used as argument extraction techniques in computational semantics, and uses examples to show how a series of connected problems needs to be solved in order to move these techniques forward to computational implementation. Some of the schemes considered are argument from expert opinion, practical reasoning, argument from negative consequences, fear appeal arguments, argument from commitment, argument from inconsistent commitments, and the circumstantial ad hominem argument. The paper shows how schemes need to be formed into clusters of sub-schemes to work toward a classification system of schemes from the bottom up, and how identification conditions for each scheme can be helpful for argument extraction.

Proceedings of the 2nd Workshop on Argumentation Mining, 2015

The paper discusses the architecture and development of an Argument Workbench, which is a interactive, integrated, modular tool set to extract, reconstruct, and visualise arguments. We consider a corpora with dispersed information across texts, making it essential to conceptually search for argument elements, topics, and terminology. The Argument Workbench is a processing cascade, developed in collaboration with DebateGraph. The tool supports an argument engineer to reconstruct arguments from textual sources, using information processed at one stage as input to a subsequent stage of analysis, and then building an argument graph. We harvest and preprocess comments; highlight argument indicators, speech act and epistemic terminology; model topics; and identify domain terminology. We use conceptual semantic search over the corpus to extract sentences relative to argument and domain terminology. The argument engineer uses the extracts for the construction of arguments in DebateGraph.

AI^3 2018 Proceedings, 2019

This paper develops a new method for reconstructing arguments in natural language by combining the linguistic representation framework of Constructive Adpositional Grammars (CxAdGrams) with the argument classi fication framework of the Periodic Table of Arguments (PTA). The method centers around the notion of 'argumentative adpositional adtree' ('arg-adtree'). After an explanation of the two frameworks involved, the method is illustrated by providing the arg-adtrees of two concrete examples of so-called 'first-order arguments'. It is argued that the resulting ad-trees provide a theoretically informed and empirically reliable reconstruction of an argumentative text or discussion. As such, the method developed in this paper is especially suitable as a point of departure for developing instruments for computer-assisted argumentation analysis.

Argument & Computation, 2012

In this article, we first present the <TextCoop> platform and the Dislog language, designed for discourse analysis with a logic and linguistic perspective. The platform has now reached a certain level of maturity which allows the recognition of a large diversity of discourse structures including general-purpose rhetorical structures as well as domain-specific discourse structures. The Dislog language is based on linguistic considerations and includes knowledge access and inference capabilities. Functionalities of the language are presented together with a method for writing discourse analysis rules. Efficiency and portability of the system over domains and languages are investigated to conclude this first part. In a second part, we analyse the different types of arguments found in several document genres, most notably: procedures, didactic texts and requirements. Arguments form a large class of discourse relations. A generic and frequently encountered form emerges from our analysis: 'reasons for conclusion' which constitutes a homogeneous family of arguments from a language, functional and conceptual point of view. This family can be viewed as a kind of proto-argument. We then elaborate its linguistic structure and show how it is implemented in <TextCoop>. We then investigate the cooperation between explanation and arguments, in particular in didactic texts where they are particularly rich and elaborated. This article ends with a prospective section that develops current and potential uses of this work and how it can be extended to the recognition of other forms of arguments.

Argumentation is becoming entrenched in a number of areas of AI as a powerful means of approaching and framing problems, and of developing novel solutions. A prime example is in multi-agent systems (MAS), where argumentation has been proposed as a means of structuring inter-agent communication, linking the definition of language protocols to the design of structures in belief databases.

2010

For argumentation useful, the means must be found overcome the knowledge acquisition bottleneck between the source material and the translation into and out of an argumentation formalism. As source material is often expressed and understood in natural language, we should consider how to translate natural language arguments. The current state of the art in argumentation does not have any formal translation method. In this paper, we discuss this lacuna. First, there are robust, wide-coverage computational linguistic systems which automatically translate multi-sentential structures into first order logic. In restricted domain of discourse, this can be used to formalise the knowledge base. We then consider how such an approach could be extended to argumentation. We propose two different models of translation based on currently available argumentation formalisms-logicbased and logic graphs. The advantage of the logic-based approach is that the arguments input into the argumentation system are sets of first order logic expressions and what is inferred from them. The consistency of each argument is assured; the relationships between the arguments in an argumentation framework graph are determined by the logic-based approach. One disadvantage is that each argument cannot have any missing expressions (enthymemes) before it can be used in the argumentation formalism. A second disadvantage is that the theory requires a complex system to determine relationships between the arguments, which is particularly relevant as arguments are added over the course of a dialogue. The logic graphs approach allows reasoning to proceed even where there are enthymemes, and it is straightforward to determine the relationships between arguments in the network and * 2009 c Adam Wyner. The author thanks Tony Hunter for comments. This paper was created 14.06.09 and revised over the summer. The current paper reflect some minor revisions. Errors rest with the author. new arguments. On the other hand, it requires that sets of logically consistent statements be recalculated every time a statement is added to the argument network. To overcome this, we suggest that research be directed to support calculations over subgraphs. Additional problems for both approaches are outlined, providing an agenda for future research.

Computational Models of Argument, 2006

This paper presents our efforts to create argument structures from meet- ing transcripts automatically. We show that unit labels of argument diagrams can be learnt and predicted by a computer with an accuracy of 78,52% and 51,43% on an unbalanced and balanced set respectively. We used a corpus of over 250 argument diagrams that was manually created by applying the

argumentation systems are formalisms for defeasible reasoning where some components remain unspecified, the structure of arguments being the main abstraction. In the dialectical process carried out to identify accepted arguments in the system some controversial situations may appear. These relate to the reintroduction of arguments into the process which cause the onset of circularity. This must be avoided in order to prevent an infinite analysis. Some systems apply the sole restriction of not allowing the introduction of previously considered arguments in an argumentation line. However, repeating an argument is not the only possible cause for the risk mentioned, as subarguments must be taken into account. In this work, we introduce an extended argumentation framework and a definition for progressive defeat path. A credulous extension is also presented.