Joint prediction in MST-style discourse parsing for argumentation mining

Peldszus, Andreas; Stede, Manfred

doi:10.18653/v1/d15-1110

Cited by 109 publications

(131 citation statements)

References 16 publications

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“…It is concerned about the argumentative role of language fragments in a full discourse [11,27,50,56]. As seen above meaning is resided in the configuration of words, but discourse markers that signal arguments to a proposition are often ambiguous or missing.…”

Section: What Makes Argumentation Mining Extra Difficult?mentioning

confidence: 99%

Argumentation mining: How can a machine acquire common sense and world knowledge?

Moens

2018

AAC

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Abstract. Argumentation mining is an advanced form of human language understanding by the machine. This is a challenging task for a machine. When sufficient explicit discourse markers are present in the language utterances, the argumentation can be interpreted by the machine with an acceptable degree of accuracy. However, in many real settings, the mining task is difficult due to the lack or ambiguity of the discourse markers, and the fact that a substantial amount of knowledge needed for the correct recognition of the argumentation, its composing elements and their relationships is not explicitly present in the text, but makes up the background knowledge that humans possess when interpreting language. In this article 1 we focus on how the machine can automatically acquire the needed common sense and world knowledge. As very few research has been done in this respect, many of the ideas proposed in this article are tentative, but start being researched.We give an overview of the latest methods for human language understanding that map language to a formal knowledge representation that facilitates other tasks (for instance, a representation that is used to visualize the argumentation or that is easily shared in a decision or argumentation support system). Most current systems are trained on texts that are manually annotated. Then we go deeper into the new field of representation learning that nowadays is very much studied in computational linguistics. This field investigates methods for representing language as statistical concepts or as vectors, allowing straightforward methods of compositionality. The methods often use deep learning and its underlying neural network technologies to learn concepts from large text collections in an unsupervised way (i.e., without the need for manual annotations). We show how these methods can help the argumentation mining process, but also demonstrate that these methods need further research to automatically acquire the necessary background knowledge and more specifically common sense and world knowledge. We propose a number of ways to improve the learning of common sense and world knowledge by exploiting textual and visual data, and touch upon how we can integrate the learned knowledge in the argumentation mining process.

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Section: What Makes Argumentation Mining Extra Difficult?mentioning

confidence: 99%

Argumentation mining: How can a machine acquire common sense and world knowledge?

Moens

2018

AAC

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“…Similarly, most existing corpora in computational argumentation are only annotated with argument components (Habernal and Gurevych, 2016a;Mochales-Palau and Moens, 2009) or argument structures (Reed et al, 2008;Peldszus and Stede, 2015) and do not include annotations of argumentative quality issues. Other resources in the field contain arguments annotated with different properties such as emotions and sarcasm (Walker et al, 2012), the type of reasoning (Reed et al, 2008) or the stance on a topic (Somasundaran and Wiebe, 2009).…”

Section: Related Workmentioning

confidence: 99%

Recognizing Insufficiently Supported Arguments in Argumentative Essays

Stab¹,

Gurevych²

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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In this paper, we propose a new task for assessing the quality of natural language arguments. The premises of a well-reasoned argument should provide enough evidence for accepting or rejecting its claim. Although this criterion, known as sufficiency, is widely adopted in argumentation theory, there are no empirical studies on its applicability to real arguments. In this work, we show that human annotators substantially agree on the sufficiency criterion and introduce a novel annotated corpus. Furthermore, we experiment with feature-rich SVMs and convolutional neural networks and achieve 84% accuracy for automatically identifying insufficiently supported arguments. The final corpus as well as the annotation guideline are freely available for encouraging future research on argument quality. 1

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“…Argumentation theory proposes a number of major models: Toulmin (1958) focuses on fine-grained roles of an argument's units, Walton et al (2008) capture the inference scheme that an argument uses, and Freeman (2011) investigates how units support or attack other units or arguments. Some computational approaches adopt one of them (Peldszus and Stede, 2015;. Others present simpler, application-oriented models that, for instance, distinguish claims and evidence only .…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 4th Workshop on Argument Mining

2017

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ii IntroductionThe goal of this workshop is to provide a follow-on forum to the last three years' Argumentation Mining workshops at ACL and NAACL, the first research forum devoted to argumentation mining in all domains of discourse.Argument mining (also, "argumentation mining", referred to as "computational argumentation" in some recent works) is a relatively new challenge in corpus-based discourse analysis that involves automatically identifying argumentative structures within discourse, e.g., the premises, conclusion, and argumentation scheme of each argument, as well as argument-subargument and argumentcounterargument relationships between pairs of arguments in the document. To date, researchers have investigated methods for argument mining in areas such as legal documents, on-line debates, product reviews, academic literature, user comments on proposed regulations, newspaper articles and court cases, as well as in dialogical domains. To date there are few corpora with annotations for argumentation mining research although corpora with annotations for argument sub-components have recently become available.Proposed applications of argumentation mining include improving information retrieval and information extraction as well as end-user visualization and summarization of arguments. Textual sources of interest include not only the formal writing of legal text, scientific writing and parliamentary records, but also a variety of informal genres such as microtext, spoken meeting transcripts, product reviews and user comments. In instructional contexts where argumentation is a pedagogically important tool for conveying and assessing students' command of course material, the written and diagrammed arguments of students (and the mappings between them) are educational data that can be mined for purposes of assessment and instruction. This is especially important given the wide-spread adoption of computer-supported peer review, computerized essay grading, and large-scale online courses and MOOCs. Success in argument mining will require interdisciplinary approaches informed by natural language processing technology, theories of semantics, pragmatics and discourse, knowledge of discourse of domains such as law and science, artificial intelligence, argumentation theory, and computational models of argumentation. In addition, it will require the creation and annotation of high-quality corpora of argumentation from different types of sources in different domains.We are looking forward to a full day workshop to exchange ideas and present ongoing research on all of the above -see you all in Copenhagen, Denmark at EMNLP 2017! AbstractIn this paper we present the dataset of 200,000+ political arguments produced in the local phase of the 2016 Chilean constitutional process. We describe the human processing of this data by the government officials, and the manual tagging of arguments performed by members of our research group. Afterwards we focus on classification tasks that mimic the human processes, comparing linear methods ...

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Joint prediction in MST-style discourse parsing for argumentation mining

Cited by 109 publications

References 16 publications

Argumentation mining: How can a machine acquire common sense and world knowledge?

Argumentation mining: How can a machine acquire common sense and world knowledge?

Recognizing Insufficiently Supported Arguments in Argumentative Essays

Proceedings of the 4th Workshop on Argument Mining

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