Otyper: A neural architecture for open named entity typing

Zheng Yuan; Douglas C Downey

Otyper: A neural architecture for open named entity typing

Zheng Yuan, Douglas C Downey

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

17 Scopus citations

Abstract

Named Entity Typing (NET) is valuable for many natural language processing tasks, such as relation extraction, question answering, knowledge base population, and co-reference resolution. Classical NET targeted a few coarse-grained types, but the task has expanded to sets of hundreds of types in recent years. Existing work in NET assumes that the target types are specified in advance, and that hand-labeled examples of each type are available. In this work, we introduce the task of Open Named Entity Typing (ONET), which is NET when the set of target types is not known in advance. We propose a neural network architecture for ONET, called OTyper, and evaluate its ability to tag entities with types not seen in training. On the benchmark FIGER(GOLD) dataset, OTyper achieves a weighted AUC-ROC score of 0.870 on unseen types, substantially outperforming pattern- and embedding-based baselines.

Original language	English (US)
Title of host publication	32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Publisher	AAAI Press
Pages	6037-6044
Number of pages	8
ISBN (Electronic)	9781577358008
State	Published - 2018
Event	32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States Duration: Feb 2 2018 → Feb 7 2018

Publication series

Name	32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Other

Other	32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Country/Territory	United States
City	New Orleans
Period	2/2/18 → 2/7/18

Funding

This work was supported in part by NSF Grant IIS-1351029 and the Allen Institute for Artificial Intelligence. We thank Oren Etzioni for suggesting we look at the ONET task. We also thank Mohammed Alam, David Demeter, Jared Fernan-dez, Thanapon Noraset, and Yiben Yang for helpful discussion. This work was supported in part by NSF Grant IIS-1351029 and the Allen Institute for Artificial Intelligence. We thank Oren Etzioni for suggesting we look at the ONET task. We also thank Mohammed Alam, David Demeter, Jared Fernandez, Thanapon Noraset, and Yiben Yang for helpful discussion.

ASJC Scopus subject areas

Artificial Intelligence

Cite this

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title = "Otyper: A neural architecture for open named entity typing",

abstract = "Named Entity Typing (NET) is valuable for many natural language processing tasks, such as relation extraction, question answering, knowledge base population, and co-reference resolution. Classical NET targeted a few coarse-grained types, but the task has expanded to sets of hundreds of types in recent years. Existing work in NET assumes that the target types are specified in advance, and that hand-labeled examples of each type are available. In this work, we introduce the task of Open Named Entity Typing (ONET), which is NET when the set of target types is not known in advance. We propose a neural network architecture for ONET, called OTyper, and evaluate its ability to tag entities with types not seen in training. On the benchmark FIGER(GOLD) dataset, OTyper achieves a weighted AUC-ROC score of 0.870 on unseen types, substantially outperforming pattern- and embedding-based baselines.",

author = "Zheng Yuan and Downey, {Douglas C}",

note = "Publisher Copyright: Copyright {\textcopyright} 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 ; Conference date: 02-02-2018 Through 07-02-2018",

year = "2018",

language = "English (US)",

series = "32nd AAAI Conference on Artificial Intelligence, AAAI 2018",

publisher = "AAAI Press",

pages = "6037--6044",

booktitle = "32nd AAAI Conference on Artificial Intelligence, AAAI 2018",

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AU - Yuan, Zheng

AU - Downey, Douglas C

PY - 2018

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AB - Named Entity Typing (NET) is valuable for many natural language processing tasks, such as relation extraction, question answering, knowledge base population, and co-reference resolution. Classical NET targeted a few coarse-grained types, but the task has expanded to sets of hundreds of types in recent years. Existing work in NET assumes that the target types are specified in advance, and that hand-labeled examples of each type are available. In this work, we introduce the task of Open Named Entity Typing (ONET), which is NET when the set of target types is not known in advance. We propose a neural network architecture for ONET, called OTyper, and evaluate its ability to tag entities with types not seen in training. On the benchmark FIGER(GOLD) dataset, OTyper achieves a weighted AUC-ROC score of 0.870 on unseen types, substantially outperforming pattern- and embedding-based baselines.

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Otyper: A neural architecture for open named entity typing

Abstract

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