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.
@InProceedings{HORBACH18.427, author = {Andrea Horbach and Manfred Pinkal}, title = "{Semi-Supervised Clustering for Short Answer Scoring}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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