Paper: Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction

ACL ID D13-1136
Title Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

This paper proposes a novel approach for rela- tion extraction from free text which is trained to jointly use information from the text and from existing knowledge. Our model is based on scoring functions that operate by learning low-dimensional embeddings of words, enti- ties and relationships from a knowledge base. We empirically show on New York Times ar- ticles aligned with Freebase relations that our approach is able to efficiently use the extra in- formation provided by a large subset of Free- base data (4M entities, 23k relationships) to improve over methods that rely on text fea- tures alone.