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CogALex-V Shared Task: HsH-Supervised – supervised similarity learning using entry wise product of context vectors

  • The CogALex-V Shared Task provides two datasets that consists of pairs of words along with a classification of their semantic relation. The dataset for the first task distinguishes only between related and unrelated, while the second data set distinguishes several types of semantic relations. A number of recent papers propose to construct a feature vector that represents a pair of words by applying a pairwise simple operation to all elements of the feature vector. Subsequently, the pairs can be classified by training any classification algorithm on these vectors. In the present paper we apply this method to the provided datasets. We see that the results are not better than from the given simple baseline. We conclude that the results of the investigated method are strongly depended on the type of data to which it is applied.

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Metadaten
Author:Rosa Tsegaye Aga, Christian WartenaORCiDGND
URN:urn:nbn:de:bsz:960-opus4-11163
Parent Title (English):Proceedings of the Workshop on Cognitive Aspects of the Lexicon, December 12, 2016, Osaka, Japan
Document Type:Conference Proceeding
Language:English
Year of Completion:2016
Release Date:2017/07/18
GND Keyword:Klassifikation
First Page:114
Last Page:118
Institutes:Fakultät III - Medien, Information und Design
Dewey Decimal Classification:020 Bibliotheks- und Informationswissenschaft
Licence (German):License LogoCreative Commons - Namensnennung 4.0