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  4. A Corpus for Suggestion Mining of German Peer Feedback
 
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A Corpus for Suggestion Mining of German Peer Feedback

Journal
Language Resources and Evaluation Conference (LREC)
Type
conference paper
Date Issued
2022
Author(s)
Rietsche, Roman  
Ritz, Eva  
Janda, Julius
Pfütze, Dominik
Research Team
IWI6, Micha Brugger, Damian Falk, Andreas Göldi, Alexander Meier, Eva Ritz
Abstract
Peer feedback in online education becomes increasingly important to meet the demand for feedback in large scale classes, such as e.g. Massive Open Online Courses (MOOCs). However, students are often not experts in how to write helpful feedback to their fellow students. In this paper, we introduce a corpus compiled from university students’ peer feedback to be able to detect suggestions on how to improve the students’ work and therefore being able to capture peer feedback helpfulness. To the best of our knowledge, this corpus is the first student peer feedback corpus in German which additionally was labelled with a new annotation scheme. The corpus consists of more than 600 written feedback (about 7,500 sentences). The utilisation of the corpus is broadly ranged from Dependency Parsing to Sentiment Analysis to Suggestion Mining, etc. We applied the latter to empirically validate the utility of the new corpus. Suggestion Mining is the extraction of sentences that contain suggestions from unstructured text. In this paper, we present a new annotation scheme to label sentences for Suggestion Mining. Two independent annotators labelled the corpus and achieved an inter-annotator agreement of 0.71. With the help of an expert arbitrator a gold standard was created. An automatic classification using BERT achieved an accuracy of 75.3%.
Language
English
Keywords
Peer Feedback
Suggestion Mining
Corpus
Annotation
HSG Classification
contribution to scientific community
Publisher place
Marseille, France
Start page
5539
End page
5547
Pages
9
Event Title
Language Resources and Evaluation Conference (LREC)
Event Location
Marseille, France
Event Date
20-25 Jun 2022
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/109467
Subject(s)

information managemen...

Division(s)

IWI - Institute of In...

Eprints ID
268190
File(s)
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Thumbnail Image

open.access

Name

JML_909.pdf

Size

503.65 KB

Format

Adobe PDF

Checksum (MD5)

9c74fb418b9ecc1249b64ce57ae18d4d

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