Collective cross-document relation extraction without labelled data.
EMNLP 2010 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference.
(pp. 1013 - 1023).
We present a novel approach to relation extraction that integrates information across documents, performs global inference and requires no labelled text. In particular, we tackle relation extraction and entity identification jointly. We use distant supervision to train a factor graph model for relation extraction based on an existing knowledge base (Freebase, derived in parts from Wikipedia). For inference we run an efficient Gibbs sampler that leads to linear time joint inference. We evaluate our approach both for an in-domain (Wikipedia) and a more realistic out-of-domain (New York Times Corpus) setting. For the in-domain setting, our joint model leads to 4% higher precision than an isolated local approach, but has no advantage over a pipeline. For the out-of-domain data, we benefit strongly from joint modelling, and observe improvements in precision of 13% over the pipeline, and 15% over the isolated baseline. © 2010 Association for Computational Linguistics.
|Title:||Collective cross-document relation extraction without labelled data|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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