Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction
This addresses the problem of extracting structured relationships from unstructured text for natural language processing applications, representing an incremental advancement.
The paper tackles relation extraction from free text by jointly using textual information and existing knowledge bases, achieving improvements over text-only methods through experiments on New York Times articles aligned with Freebase relations.
This paper proposes a novel approach for relation extraction from free text which is trained to jointly use information from the text and from existing knowledge. Our model is based on two scoring functions that operate by learning low-dimensional embeddings of words and of entities and relationships from a knowledge base. We empirically show on New York Times articles aligned with Freebase relations that our approach is able to efficiently use the extra information provided by a large subset of Freebase data (4M entities, 23k relationships) to improve over existing methods that rely on text features alone.