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 - Jan 1 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 | United States |
| City | New Orleans |
| Period | 2/2/18 → 2/7/18 |
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ASJC Scopus subject areas
- Artificial Intelligence
Cite this
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Otyper : A neural architecture for open named entity typing. / Yuan, Zheng; Downey, Douglas C.
32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press, 2018. p. 6037-6044 (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Otyper
T2 - A neural architecture for open named entity typing
AU - Yuan, Zheng
AU - Downey, Douglas C
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
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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M3 - Conference contribution
T3 - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
SP - 6037
EP - 6044
BT - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PB - AAAI press
ER -