Identifying Thesis and Conclusion Statements in Student Essays to Scaffold Peer Review

Falakmasir, Mohammad Hassan; Ashley, Kevin D.; Schunn, Christian D.; Litman, Diane

doi:10.1007/978-3-319-07221-0_31

Cited by 12 publications

(10 citation statements)

References 17 publications

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“…We followed the work of (Zhang and Litman, 2015), where unigram features (words) were used as the baseline and the SVM classifier was used. Besides unigrams, three groups of features used in revision analysis, argument mining and discourse analysis research were extracted (Location, Textual and Language) as in Table 6 (Bronner and Monz, 2012;Daxenberger and Gurevych, 2013;Burstein et al, 2001;Falakmasir et al, 2014).…”

Section: Methodsmentioning

confidence: 99%

“…Most writing-related natural language processing (NLP) research focuses on the analysis of single drafts. Examples include document-level quality assessment (Attali and Burstein, 2006;Burstein and Chodorow, 1999), discourse-level analysis and mining (Burstein et al, 2003;Falakmasir et al, 2014;Persing and Ng, 2016), and fine-grained error detection (Leacock et al, 2010;Grammarly, 2016). Less studied is the analysis of changes between drafts -a comparison of revisions and the properties of the differences.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Corpus of Annotated Revisions for Studying Argumentative Writing

Zhang¹,

Hashemi²,

Hwa³

et al. 2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

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This paper presents ArgRewrite, a corpus of between-draft revisions of argumentative essays. Drafts are manually aligned at the sentence level, and the writer's purpose for each revision is annotated with categories analogous to those used in argument mining and discourse analysis. The corpus should enable advanced research in writing comparison and revision analysis, as demonstrated via our own studies of student revision behavior and of automatic revision purpose prediction. 2 Corpus Design Decisions Consider this scenario: Alice begins her social science argumentative essay with the sentence "Electronic communication allows people to make connections beyond physical limits." An analytical system might (rightly) identify the sentence as the thesis of her essay, and an evaluative system might give the essay a low score due to this sentence's vagueness and a later lack of evidence (though Alice may not know why she received that score).

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Corpus of Annotated Revisions for Studying Argumentative Writing

Zhang¹,

Hashemi²,

Hwa³

et al. 2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

View full text Add to dashboard Cite

show abstract

“…Others focused on argument component typing, determining the type of an argument component. While the vast majority of previous works perform argument component typing at the sentence level (Rooney et al, 2012;Teufel, 1999;Burstein et al, 2003;Ong et al, 2014;Falakmasir et al, 2014;Levy et al, 2014;Lippi and Torroni, 2015;Lippi and Torroni, 2016;Rinott et al, 2015), some recent work focused on the more difficult task of typing argument components at the clause level (Park and Cardie, 2014;Goudas et al, 2015;Sardianos et al, 2015). Some researchers focused on relation identification instead.…”

Section: Related Workmentioning

confidence: 99%

End-to-End Argumentation Mining in Student Essays

Persing¹,

Ng²

2016

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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State-of-the-art systems for argumentation mining are supervised, thus relying on training data containing manually annotated argument components and the relationships between them. To eliminate the reliance on annotated data, we present a novel approach to unsupervised argument mining. The key idea is to bootstrap from a small set of argument components automatically identified using simple heuristics in combination with reliable contextual cues. Results on a Stab and Gurevych's corpus of 402 essays show that our unsupervised approach rivals two supervised baselines in performance and achieves 73.5−83.7% of the performance of a state-of-the-art neural approach.

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“…In this paper we explore whether the features used in Wikipedia revision classification can be adapted to the classification of different categories of revisions in our work. We also utilize features from research on argument mining and discourse parsing Sporleder and Lascarides, 2008;Falakmasir et al, 2014;Braud and Denis, 2014) and evaluate revision classification both intrinsically and extrinsically. Finally, we explore end-to-end revision processing by combining automatic revision extraction and categorization via automatic classification in a pipelined manner.…”

Section: Revision Classificationmentioning

confidence: 99%

Annotation and Classification of Argumentative Writing Revisions

Zhang¹,

Litman²

2015

Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications

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Prior work on revision identification typically uses a pipeline method: revision extraction is first conducted to identify the locations of revisions and revision classification is then conducted on the identified revisions. Such a setting propagates the errors of the revision extraction step to the revision classification step. This paper proposes an approach that identifies the revision location and the revision type jointly to solve the issue of error propagation. It utilizes a sequence representation of revisions and conducts sequence labeling for revision identification. A mutation-based approach is utilized to update identification sequences. Results demonstrate that our proposed approach yields better performance on both revision location extraction and revision type classification compared to a pipeline baseline. 1 The categories are defined according to Toulmin's argumentation model (Toulmin, 2003).2 Surface include changes such as spelling correction and sentence reorderings that do not change a paper's content.

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Identifying Thesis and Conclusion Statements in Student Essays to Scaffold Peer Review

Cited by 12 publications

References 17 publications

A Corpus of Annotated Revisions for Studying Argumentative Writing

A Corpus of Annotated Revisions for Studying Argumentative Writing

End-to-End Argumentation Mining in Student Essays

Annotation and Classification of Argumentative Writing Revisions

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