Improved Relation Extraction with Feature-Rich Compositional Embedding Models
This work addresses relation extraction for natural language processing, offering an incremental improvement by integrating existing methods.
The paper tackled relation extraction by proposing a Feature-rich Compositional Embedding Model (FCM) that combines hand-crafted features with learned word embeddings, resulting in state-of-the-art performance on ACE 2005 and SemEval 2010 tasks.
Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component 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 embeddings. The model is able to directly tackle the difficulties met by traditional compositional embeddings models, such as handling arbitrary types of sentence annotations and utilizing global information for composition. We test the proposed model on two relation extraction tasks, and demonstrate that our model outperforms 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.