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        <title>Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees
%A Katiyar, Arzoo
%A Cardie, Claire
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F katiyar-cardie-2017-going
%X 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.
%R 10.18653/v1/P17-1085
%U https://aclanthology.org/P17-1085
%U https://doi.org/10.18653/v1/P17-1085
%P 917-928

Markdown (Informal)

[Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees](https://aclanthology.org/P17-1085) (Katiyar & Cardie, ACL 2017)

• Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees (Katiyar & Cardie, ACL 2017)

ACL

• Arzoo Katiyar and Claire Cardie. 2017. Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 917–928, Vancouver, Canada. Association for Computational Linguistics.