Word embeddings have gained significant attention as learnable
representations of semantic relations between words, and have been shown to
improve upon the results of traditional word representations. However, little
effort has been devoted to using embeddings for the retrieval of entity
associations beyond pairwise relations. In this paper, we use popular embedding
methods to train vector representations of an entity-annotated news corpus, and
evaluate their performance for the task of predicting entity participation in
news events versus a traditional word cooccurrence network as a baseline. To
support queries for events with multiple participating entities, we test a
number of combination modes for the embedding vectors. While we find that even
the best combination modes for word embeddings do not quite reach the
performance of the full cooccurrence network, especially for rare entities, we
observe that different embedding methods model different types of relations,
thereby indicating the potential for ensemble methods.

本研究探讨了基于流行的嵌入方法训练矢量表示的实体注释新闻语料库的应用，以预测实体参与新闻事件，并测试了一些嵌入矢量的组合模式以支持多参与实体事件查询。我们发现，虽然单一的基于词嵌入的组合模式并不能完全达到传统词共现网络的水平，但不同的嵌入方法对不同类型的关系进行了建模，因此有可能实现集成方法。