Difference between revisions of "Automated Classification of Stance in Student Essays: an Approach Using Stance Target Information and the Wikipedia Link-Based Measure"
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| + | | title = Automated Classification of Stance in Student Essays: an Approach Using Stance Target Information and the Wikipedia Link-Based Measure | ||
| + | | date = 2014 | ||
| + | | authors = [[Adam Faulkner]] | ||
| + | | link = | ||
| + | }} | ||
'''Automated Classification of Stance in Student Essays: an Approach Using Stance Target Information and the Wikipedia Link-Based Measure''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Adam Faulkner]]. | '''Automated Classification of Stance in Student Essays: an Approach Using Stance Target Information and the Wikipedia Link-Based Measure''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Adam Faulkner]]. | ||
== Overview == | == Overview == | ||
Authors present a new approach to the automated classification of document-level argument stance, a relatively under-researched sub-task of Sentiment Analysis. In place of the noisy online debate data currently used in stance classification research, a corpus of student essays annotated for essay-level stance is constructed for use in a series of classification experiments. A novel set of [[features]] designed to capture the stance, stance targets, and topical relationships between the essay prompt and the student's essay is described. Models trained on this feature set showed significant increases in accuracy relative to two high baselines. | Authors present a new approach to the automated classification of document-level argument stance, a relatively under-researched sub-task of Sentiment Analysis. In place of the noisy online debate data currently used in stance classification research, a corpus of student essays annotated for essay-level stance is constructed for use in a series of classification experiments. A novel set of [[features]] designed to capture the stance, stance targets, and topical relationships between the essay prompt and the student's essay is described. Models trained on this feature set showed significant increases in accuracy relative to two high baselines. | ||
Revision as of 08:50, 26 June 2020
Authors | Adam Faulkner |
|---|---|
Publication date | 2014 |
Links |
Automated Classification of Stance in Student Essays: an Approach Using Stance Target Information and the Wikipedia Link-Based Measure - scientific work related to Wikipedia quality published in 2014, written by Adam Faulkner.
Overview
Authors present a new approach to the automated classification of document-level argument stance, a relatively under-researched sub-task of Sentiment Analysis. In place of the noisy online debate data currently used in stance classification research, a corpus of student essays annotated for essay-level stance is constructed for use in a series of classification experiments. A novel set of features designed to capture the stance, stance targets, and topical relationships between the essay prompt and the student's essay is described. Models trained on this feature set showed significant increases in accuracy relative to two high baselines.
