Improved Relation Extraction with Feature-Rich Compositional Embedding Models

Understand

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.