  We present a novel learning method for word embeddings designed for relation
classification. Our word embeddings are trained by predicting words between
noun pairs using lexical relation-specific features on a large unlabeled
corpus. This allows us to explicitly incorporate relation-specific information
into the word embeddings. The learned word embeddings are then used to
construct feature vectors for a relation classification model. On a
well-established semantic relation classification task, our method
significantly outperforms a baseline based on a previously introduced word
embedding method, and compares favorably to previous state-of-the-art models
that use syntactic information or manually constructed external resources.
