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EMNLP
2010

Collective Cross-Document Relation Extraction Without Labelled Data

9 years 9 months ago
Collective Cross-Document Relation Extraction Without Labelled Data
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 indomain (Wikipedia) and a more realistic outof-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.
Limin Yao, Sebastian Riedel, Andrew McCallum
Added 02 Mar 2011
Updated 02 Mar 2011
Type Journal
Year 2010
Where EMNLP
Authors Limin Yao, Sebastian Riedel, Andrew McCallum
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