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Identifying Entity Properties from Text with Zero-shot Learning

Authors:

Wiradee Imrattanatrai,

Makoto P. Kato,

Masatoshi YoshikawaAuthors Info & Claims

SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 195 - 204

https://doi.org/10.1145/3331184.3331220

Published: 18 July 2019 Publication History

Abstract

We propose a method for identifying a set of entity properties from text. Identifying entity properties is similar to a relation extraction task that can be cast as a classification of sentences. Normally, this task can be achieved by distant supervised learning by automatically preparing training sentences for each property; however, it is impractical to prepare training sentences for every property. Therefore, we describe a zero-shot learning problem for this task and propose a neural network-based model that does not rely on a complete training set comprising training sentences for every property. To achieve this, we utilize embeddings of properties obtained from a knowledge graph embedding using different components of a knowledge graph structure. The embeddings of properties are combined with the model to enable identification of properties with no available training sentences. By using our newly constructed dataset as well as an existing dataset, experiments revealed that our model achieved a better performance for properties with no training sentences, relative to baseline results, even comparable to that achieved for properties with training sentences.

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Index Terms

Identifying Entity Properties from Text with Zero-shot Learning
1. Information systems

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cover image ACM Conferences

SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2019

1512 pages

ISBN:9781450361729

DOI:10.1145/3331184

General Chairs:
Benjamin Piwowarski
CNRS - Sorbonne Universite, France
,
Max Chevalier
Universite de Toulouse, CNRS, France
,
Eric Gaussier
Universite Grenoble Alpes, CNRS, France
,
Program Chairs:
Yoelle Maarek
Amazon Research, Israel
,
Jian-Yun Nie
University of Montreal, Canada
,
Falk Scholer
RMIT University, Australia

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Published: 18 July 2019

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SIGIR '19: The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 21 - 25, 2019

Paris, France

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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https://dl.acm.org/doi/10.1109/TMM.2023.3236211
Li YLiu ZYao LChang X(2023)Attribute-Modulated Generative Meta Learning for Zero-Shot LearningIEEE Transactions on Multimedia10.1109/TMM.2021.313921125(1600-1610)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2021.3139211
Chen JGeng YChen ZPan JHe YZhang WHorrocks IChen H(2023)Zero-Shot and Few-Shot Learning With Knowledge Graphs: A Comprehensive SurveyProceedings of the IEEE10.1109/JPROC.2023.3279374111:6(653-685)Online publication date: Jun-2023
https://doi.org/10.1109/JPROC.2023.3279374
Geng YChen JZhuang XChen ZPan JLi JYuan ZChen H(2023)Benchmarking knowledge-driven zero-shot learningJournal of Web Semantics10.1016/j.websem.2022.10075775(100757)Online publication date: Jan-2023
https://doi.org/10.1016/j.websem.2022.100757
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
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Cheng HLiao LHu LNie L(2022)Multi-Relation Extraction via A Global-Local Graph Convolutional NetworkIEEE Transactions on Big Data10.1109/TBDATA.2022.3144151(1-1)Online publication date: 2022
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Kong DLi XWang SLi JYin B(2022)Learning visual-and-semantic knowledge embedding for zero-shot image classificationApplied Intelligence10.1007/s10489-022-03443-1Online publication date: 6-May-2022
https://doi.org/10.1007/s10489-022-03443-1

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