Multilingual Relation Extraction using Compositional Universal Schema

Understand

Universal schema builds a knowledge base (KB) of entities and relations by jointly embedding all relation types from input KBs as well as textual patterns expressing relations from raw text.

• In most previous applications of universal schema, each textual pattern is represented as a single embedding, preventing generalization to unseen patterns.
• Recent work employs a neural network to capture patterns' compositional semantics, providing generalization to all possible input text.
• In response, this paper introduces significant further improvements to the coverage and flexibility of universal schema relation extraction: predictions for entities unseen in training and multilingual transfer learning to domains with no annotation.