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Harmonization Sometimes Harms


Klenner, Manfred; Göhring, Anne; Amsler, Michael (2020). Harmonization Sometimes Harms. In: Proceedings of the 5th Swiss Text Analytics Conference (SwissText) & 16th Conference on Natural Language Processing (KONVENS), Winterthur, 23 June 2020 - 25 June 2020.

Abstract

In this paper we argue that harmonization is not the preferred way to produce a gold standard in all cases. Neither does a majority vote based harmonization produce an appropriate gold standard centroid, nor would a mere centroid be a good basis for training a system that reproduces prototypical user reactions given some understanding task. We discuss these claims in the context of sentiment inference.

Abstract

In this paper we argue that harmonization is not the preferred way to produce a gold standard in all cases. Neither does a majority vote based harmonization produce an appropriate gold standard centroid, nor would a mere centroid be a good basis for training a system that reproduces prototypical user reactions given some understanding task. We discuss these claims in the context of sentiment inference.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Scopus Subject Areas:Physical Sciences > General Computer Science
Language:English
Event End Date:25 June 2020
Deposited On:27 Jan 2021 14:49
Last Modified:02 Feb 2021 11:20
Publisher:swisstext-and-konvens-2020
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:http://swisstext-and-konvens-2020.org

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