Compositional embedding models build a representation (or embedding) for a linguistic structure based on its compo-nent word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement. The key idea is to combine both (unlexicalized) hand-crafted features with learned word em-beddings. The model is able to directly tackle the difficulties met by traditional compositional embeddings models, such as handling arbitrary types of sentence an-notations and utilizing global information for composition. We test the proposed model on two relation extraction tasks, and demonstrate that our model outper-forms both previous compositional models and traditional feature rich models on the ACE 2005 relation extraction task, and the SemEval 2010 relation classification task. The combination of our model and a log-linear classifier with hand-crafted features gives state-of-the-art results. We made our implementation available for general use1. 1

We study the application of word embed-dings to generate semantic representations for the domain adaptation problem of re-lation extraction (RE) in the tree kernel-based method. We systematically evaluate various techniques to generate the seman-tic representations and demonstrate that they are effective to improve the general-ization performance of a tree kernel-based relation extractor across domains (up to 7 % relative improvement). In addition, we compare the tree kernel-based and the feature-based method for RE in a compat-ible way, on the same resources and set-tings, to gain insights into which kind of system is more robust to domain changes. Our results and error analysis shows that the tree kernel-based method outperforms the feature-based approach. 1