John Lawrence


2019

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An Online Annotation Assistant for Argument Schemes
John Lawrence | Jacky Visser | Chris Reed
Proceedings of the 13th Linguistic Annotation Workshop

Understanding the inferential principles underpinning an argument is essential to the proper interpretation and evaluation of persuasive discourse. Argument schemes capture the conventional patterns of reasoning appealed to in persuasion. The empirical study of these patterns relies on the availability of data about the actual use of argumentation in communicative practice. Annotated corpora of argument schemes, however, are scarce, small, and unrepresentative. Aiming to address this issue, we present one step in the development of improved datasets by integrating the Argument Scheme Key – a novel annotation method based on one of the most popular typologies of argument schemes – into the widely used OVA software for argument analysis.

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Argument Mining: A Survey
John Lawrence | Chris Reed
Computational Linguistics, Volume 45, Issue 4 - December 2019

Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language. Understanding argumentative structure makes it possible to determine not only what positions people are adopting, but also why they hold the opinions they do, providing valuable insights in domains as diverse as financial market prediction and public relations. This survey explores the techniques that establish the foundations for argument mining, provides a review of recent advances in argument mining techniques, and discusses the challenges faced in automatically extracting a deeper understanding of reasoning expressed in language in general.

2018

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Intertextual Correspondence for Integrating Corpora
Jacky Visser | Rory Duthie | John Lawrence | Chris Reed
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Mining Argumentative Structure from Natural Language text using Automatically Generated Premise-Conclusion Topic Models
John Lawrence | Chris Reed
Proceedings of the 4th Workshop on Argument Mining

This paper presents a method of extracting argumentative structure from natural language text. The approach presented is based on the way in which we understand an argument being made, not just from the words said, but from existing contextual knowledge and understanding of the broader issues. We leverage high-precision, low-recall techniques in order to automatically build a large corpus of inferential statements related to the text’s topic. These statements are then used to produce a matrix representing the inferential relationship between different aspects of the topic. From this matrix, we are able to determine connectedness and directionality of inference between statements in the original text. By following this approach, we obtain results that compare favourably to those of other similar techniques to classify premise-conclusion pairs (with results 22 points above baseline), but without the requirement of large volumes of annotated, domain specific data.

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Using Complex Argumentative Interactions to Reconstruct the Argumentative Structure of Large-Scale Debates
John Lawrence | Chris Reed
Proceedings of the 4th Workshop on Argument Mining

In this paper we consider the insights that can be gained by considering large scale argument networks and the complex interactions between their constituent propositions. We investigate metrics for analysing properties of these networks, illustrating these using a corpus of arguments taken from the 2016 US Presidential Debates. We present techniques for determining these features directly from natural language text and show that there is a strong correlation between these automatically identified features and the argumentative structure contained within the text. Finally, we combine these metrics with argument mining techniques and show how the identification of argumentative relations can be improved by considering the larger context in which they occur.

2016

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A Corpus of Argument Networks: Using Graph Properties to Analyse Divisive Issues
Barbara Konat | John Lawrence | Joonsuk Park | Katarzyna Budzynska | Chris Reed
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Governments are increasingly utilising online platforms in order to engage with, and ascertain the opinions of, their citizens. Whilst policy makers could potentially benefit from such enormous feedback from society, they first face the challenge of making sense out of the large volumes of data produced. This creates a demand for tools and technologies which will enable governments to quickly and thoroughly digest the points being made and to respond accordingly. By determining the argumentative and dialogical structures contained within a debate, we are able to determine the issues which are divisive and those which attract agreement. This paper proposes a method of graph-based analytics which uses properties of graphs representing networks of arguments pro- & con- in order to automatically analyse issues which divide citizens about new regulations. By future application of the most recent advances in argument mining, the results reported here will have a chance to scale up to enable sense-making of the vast amount of feedback received from citizens on directions that policy should take.

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The CASS Technique for Evaluating the Performance of Argument Mining
Rory Duthie | John Lawrence | Katarzyna Budzynska | Chris Reed
Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

2015

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Combining Argument Mining Techniques
John Lawrence | Chris Reed
Proceedings of the 2nd Workshop on Argumentation Mining

2014

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Mining Arguments From 19th Century Philosophical Texts Using Topic Based Modelling
John Lawrence | Chris Reed | Colin Allen | Simon McAlister | Andrew Ravenscroft
Proceedings of the First Workshop on Argumentation Mining