  Due to their ease of use and high accuracy, Word2Vec (W2V) word embeddings
enjoy great success in the semantic representation of words, sentences, and
whole documents as well as for semantic similarity estimation. However, they
have the shortcoming that they are directly extracted from a surface
representation, which does not adequately represent human thought processes and
also performs poorly for highly ambiguous words. Therefore, we propose Semantic
Concept Embeddings (CE) based on the MultiNet Semantic Network (SN) formalism,
which addresses both shortcomings. The evaluation on a marketing target group
distribution task showed that the accuracy of predicted target groups can be
increased by combining traditional word embeddings with semantic CEs.
