  We address the problem of tuning word embeddings for specific use cases and
domains. We propose a new method that automatically combines multiple
domain-specific embeddings, selected from a wide range of pre-trained
domain-specific embeddings, to improve their combined expressive power. Our
approach relies on two key components: 1) a ranking function, based on a new
embedding similarity measure, that selects the most relevant embeddings to use
given a domain and 2) a dimensionality reduction method that combines the
selected embeddings to produce a more compact and efficient encoding that
preserves the expressiveness. We empirically show that our method produces
effective domain-specific embeddings that consistently improve the performance
of state-of-the-art machine learning algorithms on multiple tasks, compared to
generic embeddings trained on large text corpora.
