Title
Going Out On A Limb: Joint Extraction Of Entity Mentions And Relations Without Dependency Trees
Abstract
We present a novel attention-based recurrent neural network for joint extraction of entity mentions and relations. We show that attention along with long short term memory (LSTM) network can extract semantic relations between entity mentions without having access to dependency trees. Experiments on Automatic Content Extraction (ACE) corpora show that our model significantly outperforms feature-based joint model by Li and Ji (2014). We also compare our model with an end-to-end tree-based LSTM model (SPTree) by Miwa and Bansal (2016) and show that our model performs within 1% on entity mentions and 2% on relations. Our fine-grained analysis also shows that our model performs significantly better on AGENT-ARTIFACT relations, while SPTree performs better on PHYSICAL and PART-WHOLE relations.
Year
DOI
Venue
2017
10.18653/v1/P17-1085
PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1
Field
DocType
Volume
Computer science,Limb joint,Natural language processing,Artificial intelligence
Conference
P17-1
Citations 
PageRank 
References 
11
0.50
20
Authors
2
Name
Order
Citations
PageRank
Arzoo Katiyar1211.99
Claire Cardie25591555.20