Semi-Supervised Clustering for Short Answer Scoring

This paper investigates the use of semi-supervised clustering for Short Answer Scoring (SAS). In SAS, clustering techniques are an attractive alternative to classification because they provide structured groups of answers in addition to a score. Previous approaches use unsupervised clustering and have teachers label some items after clustering. We propose to re-allocate some of the human annotation effort to before and during the clustering process for (i) feature selection, (ii) for creating pairwise constraints and (iii) for metric learning. Our methods improve clustering performance substantially from 0.504 kappa for unsupervised clustering to 0.566.

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Conference:
11th International Conference on Language Resources and Evaluation - LREC 2018; Miyazaki; Japan; 7 - 12 May 2018
Date Issued:
2018
URN:
urn:nbn:de:hbz:464-20211130-131339-8
DOI:
10.17185/duepublico/72289
Language:
English
Type of Resource:
Text
Keywords:
Short-Answer Scoring; Clustering; Computer-Assisted Language Learning
Collection:
E-Publications
Subject categories of the Deutsche Nationalbibliografie:
004 Data processing Computer sciences
URL:
https://aclanthology.org/L18-1641
Hosting institution:
Faculty of Engineering, Computer Science and Applied Cognitive Science, Computer Science, Language Technology Lab
Info on primary publication:
Horbach, Andrea/Pinkal, Manfred (2018): Semi-Supervised Clustering for Short Answer Scoring. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), pp. 4065 - 4071. European Language Resources Association. https://aclanthology.org/L18-1641

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