We trained 200-dimensional attr2vec embeddings using news topics as additional contextual information and a linear bag-of-words approach -i.e., each row in the feature matrix consists of a pair of words and the topic of the news article where such pair has been observed (see the first and the last group of columns for the example in Figure 1).
we used the same collection of ∼8M news articles presented in Section 4.1 and we considered the following two article topics: general news stories (G) and sport news (SP O).