Proceedings-Artikel

JEDI: Joint Entity and Relation Detection using Type Inference

Johannes Kirschnick; Holmer Hemsen; Volker Markl
In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Annual Meeting of the Association for Computational Linguistics (ACL-16), 54th, August 7-12, Berlin, Germany, ACL, 2016.

Abstract

Freebase contains entities and relation information but is highly incomplete. Relevant information is ubiquitous in web text, but extraction deems challenging. We present JEDI, an automated system to jointly extract typed named entities and Freebase relations using dependency pattern from text. An innovative method for constraint solving on entity types of multiple relations is used to disambiguate pattern. The high precision in the evaluation supports our claim that we can detect entities and relations together, alleviating the need to train a custom classifier for an entity type.

Projekte

SD4M
SDW

Weitere Links

BibTeX

http://jedi.textmining.tu-berlin.de/

https://github.com/jkirsch/jedi

acl2016.pdf