TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Aga, Rosa Tsegaye
A1  - Wartena, Christian
T1  - CogALex-V Shared Task: HsH-Supervised – supervised similarity learning using entry wise product of context vectors
T2  - Proceedings of the Workshop on Cognitive Aspects of the Lexicon, December 12, 2016, Osaka, Japan
N2  - 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.
KW  - Klassifikation
Y1  - 2016
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:960-opus4-11163
U6  - https://doi.org/10.25968/opus-1116
DO  - https://doi.org/10.25968/opus-1116
SP  - 114
EP  - 118
ER  -