End-to-End Argumentation Mining in Student Essays

Persing, Isaac; Ng, Vincent

doi:10.18653/v1/n16-1164

Cited by 86 publications

(98 citation statements)

References 21 publications

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“…For applications, however, an automatic segmentation is obligatory. Recently, three approaches have been presented that deal with the unit segmentation of persuasive essays: Persing and Ng (2016) rely on handcrafted rules based on the parse tree of a sentence to identify segments; Stab (2017) uses sequence modeling based on sophisticated features to classify the argumentativeness of each single word based on its surrounding words; and Eger et al (2017) employ a deep learning architecture that uses different features to do the same classification based on the entire essay. So far, however, it is neither clear what the best segmentation approach is, nor how different features and models generalize across domains and genres of argumentative texts.…”

Section: Introductionmentioning

confidence: 99%

Unit Segmentation of Argumentative Texts

Ajjour

Chen

Kiesel

et al. 2017

Proceedings of the 4th Workshop on Argument Mining

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The segmentation of an argumentative text into argument units and their nonargumentative counterparts is the first step in identifying the argumentative structure of the text. Despite its importance for argument mining, unit segmentation has been approached only sporadically so far. This paper studies the major parameters of unit segmentation systematically. We explore the effectiveness of various features, when capturing words separately, along with their neighbors, or even along with the entire text. Each such context is reflected by one machine learning model that we evaluate within and across three domains of texts. Among the models, our new deep learning approach capturing the entire text turns out best within all domains, with an F-score of up to 88.54. While structural features generalize best across domains, the domain transfer remains hard, which points to major challenges of unit segmentation.

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Section: Introductionmentioning

confidence: 99%

Unit Segmentation of Argumentative Texts

Ajjour

Chen

Kiesel

et al. 2017

Proceedings of the 4th Workshop on Argument Mining

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show abstract

“…Most existing work on argumentative relation focuses on classifying relations between argument units of monologic argumentation, from a single text. One line of research (Stab and Gurevych, 2014;Persing and Ng, 2016;Nguyen and Litman, 2016) extracted argument units and predicted relations (i.e., support, attack, none) between argument units in persuasive student essays. Peldszus and Stede (2015) identified the argument structure of short texts in a bilingual corpus.…”

Section: Related Workmentioning

confidence: 99%

Argument Relation Classification Using a Joint Inference Model

Hou

Jochim

2017

Proceedings of the 4th Workshop on Argument Mining

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“…Previous work employs rule-based identification (Persing and Ng, 2016), featurebased classification , conditional random fields Stab, 2017), or deep neural networks (Eger et al, 2017). Especially the most recent approaches by Stab and Eger et al rely on sophisticated structural, syntactical, and lexical features.…”

Section: Related Workmentioning

confidence: 99%

“…A frequent choice is one of the two versions of the Argument Annotated Essay Corpus Stab, 2017), which is studied by Persing and Ng (2016), Eger et al (2017), Stab (2017) himself, and also by us. However, for a unit segmentation algorithm to be integrated into applications, it has to work robustly also for new texts from other domains.…”

Section: Related Workmentioning

confidence: 99%

“…Arg (6) Pro GM crops generate greater wealth for farming communities. (Stab and Gurevych, 2014;Persing and Ng, 2016; extracted argument units and predicted relations (i.e., support, attack, none) between argument units in persuasive student essays. Peldszus and Stede (2015) identified the argument structure of short texts in a bilingual corpus.…”

Section: Introductionmentioning

confidence: 99%

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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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End-to-End Argumentation Mining in Student Essays

Cited by 86 publications

References 21 publications

Unit Segmentation of Argumentative Texts

Unit Segmentation of Argumentative Texts

Argument Relation Classification Using a Joint Inference Model

Proceedings of the 4th Workshop on Argument Mining

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