Combining Argument Mining Techniques

Lawrence, John; Reed, Chris

doi:10.3115/v1/w15-0516

Cited by 54 publications

(44 citation statements)

References 17 publications

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“…This could reflect that in case dirimens copulatio is indeed used to forcefully introduce a claim, the speaker anticipates this claim to be met with doubt, and therefore supports it with argumentation. 26 The high levels of support and conflict align with the idea that dirimens copulatio demands attention, and suggests that where this figure occurs, not only will it likely contribute to the argumentation structure, it will also likely be a relatively central point in the dialogue as a wholepotentially a useful characteristic for automated text summarisation [1].…”

Section: Results On Dirimens Copulatiomentioning

confidence: 99%

“…On the assumption that the figures can play a functional role in establishing argumentation structure, the formal dimension of rhetorical figures can be used, e.g., as a feature in machine learning approaches to argument mining. With a particular focus on concerted approaches to argument mining -in which diverse features and techniques, and insights from various disciplines and perspectives are combined to achieve the best results [26] -the first step would be to show that the forms of rhetorical figures can be identified automatically. As we have demonstrated, indeed, the very definition of various figures serves as the algorithm for their identification.…”

Section: Resultsmentioning

confidence: 99%

“…Previous efforts to overcome this issue have looked at discourse indicators [53] -explicit linguistic expressions signalling the discursive role played by an utterance [49] -as easily identified patterns that require no prior annotation [26,55]. Whilst discourse indicators have been shown to be a reliable way of determining argumentative structure, the majority of arguments is not explicitly signalled by means of such an indicator, limiting their effectiveness in argument mining.…”

Section: Argument Miningmentioning

confidence: 99%

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Harnessing rhetorical figures for argument mining

Lawrence

Visser

Reed

2017

AAC

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Abstract. The generalised, automated reconstruction of the reasoning structures underlying persuasive communication is an enormously challenging task. While this work in argument mining is increasingly informed by the rich tradition of argumentation studies outside the computational field, the rhetorical perspective on argumentation is thus far largely ignored. To explore the application of rhetorical insights in argument mining, we conduct a pilot study on the connection between rhetorical figures and argumentation structure. Rhetorical figures are linguistic devices that perform a variety of functions in argumentative discourse. The textual form of some of these figures is easy to identify automatically, such that an established connection between the figure and a preponderance of argumentative content would improve the performance of argument mining techniques. Furthermore, the automated mining of rhetorical figures could be used as an empirical, corpus-based testing ground for the claims made about these figures in the rhetorical literature. In the pilot study, we explore the connection between eight rhetorical figures the forms of which we expect to be relatively easy to identify computationally, and argumentation structure (concretely, we consider the six schemes 'anadiplosis', 'epanaphora', 'epistrophe', 'epizeuxis', 'eutrepismus', and 'polyptoton', and the two tropes 'antithesis' and 'dirimens copulatio', and relate their occurrences to relations of inference and conflict). The data of the study is collected in the MM2012c corpus of 39,694 words of argumentatively annotated transcripts from the BBC Radio 4's Moral Maze discussion program. We show that some of the figures indeed correspond to passages of high argumentative density, relative to the text as a whole.

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Section: Results On Dirimens Copulatiomentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Argument Miningmentioning

confidence: 99%

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Harnessing rhetorical figures for argument mining

Lawrence

Visser

Reed

2017

AAC

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“…(Bilu et al, 2015;Kiesel et al, 2015;Nguyen and Litman, 2015;Yanase et al, 2015)) and five papers used precision, recall and F1 score (cf. (Lawrence and Reed, 2015;Sobhani et al, 2015;Park et al, 2015;Nguyen and Litman, 2015;Peldszus and Stede, 2015)) with one paper using a macro-averaged F1. What is required in the area of argument mining is a coherent model to give results for both annotator agreement but also the results of argument mining.…”

Section: Related Workmentioning

confidence: 99%

The CASS Technique for Evaluating the Performance of Argument Mining

Duthie¹,

Lawrence²,

Budzyńska³

et al. 2016

Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

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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.

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“…Previous work (Lawrence and Reed, 2015) has shown that discourse indicators such as because and therefore are very reliable predictors of argument structure. Unfortunately they are also rather rare, occurring with fewer than 10% of argumentative inference steps.…”

Section: Introductionmentioning

confidence: 99%

Mining Argumentative Structure from Natural Language text using Automatically Generated Premise-Conclusion Topic Models

Lawrence¹,

Reed²

2017

Proceedings of the 4th Workshop on Argument Mining

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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 highprecision, 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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Combining Argument Mining Techniques

Cited by 54 publications

References 17 publications

Harnessing rhetorical figures for argument mining

Harnessing rhetorical figures for argument mining

The CASS Technique for Evaluating the Performance of Argument Mining

Mining Argumentative Structure from Natural Language text using Automatically Generated Premise-Conclusion Topic Models

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