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Neural Zero-Shot Fine-Grained Entity Typing

Authors:

Yankun Ren,

Jianbin Lin,

Jun ZhouAuthors Info & Claims

WWW '20: Companion Proceedings of the Web Conference 2020

Pages 846 - 847

https://doi.org/10.1145/3366424.3382725

Published: 23 April 2020 Publication History

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Abstract

Fine-grained entity typing is a task to assign types to entity mentions dependent on mentions’ context. Due to the heavy work of human annotation, high quality training data is always not enough, zero-shot fine-grained entity typing becomes important. Previous zero-shot works rely on hand-crafted features and suffers from noisy distant supervision induced training data. In this paper, we propose a Neural Zero-Shot Fine-Grained Entity Typing (NZFET) model. NZFET is an end-to-end neural model free from hand-crafted features. The entity type attention in NZFET makes model focus on information relevent to the entity type. In our experiments, NZFET obtains better results on popular datasets than previous works. And we show experimentally that our method is robust against noisy training data.

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Cited By

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Wang BZhang XWang JGao CDuan QLi L(2023)Fine-grained cybersecurity entity typing based on multimodal representation learningMultimedia Tools and Applications10.1007/s11042-023-16839-z83:10(30207-30232)Online publication date: 15-Sep-2023
https://doi.org/10.1007/s11042-023-16839-z
Dash SBagchi SMihindukulasooriya NGliozzo A(2023)Linking Tabular Columns to Unseen OntologiesThe Semantic Web – ISWC 202310.1007/978-3-031-47240-4_27(502-521)Online publication date: 27-Oct-2023
https://doi.org/10.1007/978-3-031-47240-4_27
Hu YChapman AWen GHall D(2022)What Can Knowledge Bring to Machine Learning?—A Survey of Low-shot Learning for Structured DataACM Transactions on Intelligent Systems and Technology10.1145/351003013:3(1-45)Online publication date: 3-Mar-2022
https://dl.acm.org/doi/10.1145/3510030

Index Terms

Neural Zero-Shot Fine-Grained Entity Typing
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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We study the problem of few-shot Fine-grained Entity Typing (FET), where only a few annotated entity mentions with contexts are given for each entity type. Recently, prompt-based tuning has demonstrated superior performance to standard fine-tuning in few-...
A Neighborhood-Attention Fine-grained Entity Typing for Knowledge Graph Completion
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Knowledge graph (KG) entity typing focuses on inferring possible entity type instances, which is a significant subtask of knowledge graph completion (KGC). Existing entity typing methods usually exploit the entity representation to model the ...
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Abstract
Knowledge graphs are often highly incomplete due to their large sizes and one major task for knowledge graph completion is entity typing, that is to predict missing types of entities or vice versa. It is especially challenging to perform entity ...

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Published In

WWW '20: Companion Proceedings of the Web Conference 2020

April 2020

854 pages

ISBN:9781450370240

DOI:10.1145/3366424

Editors:
Amal El Fallah Seghrouchni
Sorbonne University, France
,
Gita Sukthankar
University of Central Florida, United States
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 23 April 2020

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WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Wang BZhang XWang JGao CDuan QLi L(2023)Fine-grained cybersecurity entity typing based on multimodal representation learningMultimedia Tools and Applications10.1007/s11042-023-16839-z83:10(30207-30232)Online publication date: 15-Sep-2023
https://doi.org/10.1007/s11042-023-16839-z
Dash SBagchi SMihindukulasooriya NGliozzo A(2023)Linking Tabular Columns to Unseen OntologiesThe Semantic Web – ISWC 202310.1007/978-3-031-47240-4_27(502-521)Online publication date: 27-Oct-2023
https://doi.org/10.1007/978-3-031-47240-4_27
Hu YChapman AWen GHall D(2022)What Can Knowledge Bring to Machine Learning?—A Survey of Low-shot Learning for Structured DataACM Transactions on Intelligent Systems and Technology10.1145/351003013:3(1-45)Online publication date: 3-Mar-2022
https://dl.acm.org/doi/10.1145/3510030

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Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance Generation

A Neighborhood-Attention Fine-grained Entity Typing for Knowledge Graph Completion

Zero-Shot Entity Typing in Knowledge Graphs

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