Path-based attention neural model for fine-grained entity typing

Denghui Zhang, Manling Li, Pengshan Cai, Yantao Jia, Yuanzhuo Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Fine-grained entity typing aims to assign entity mentions in the free text with types arranged in a hierarchical structure. It suffers from the label noise in training data generated by distant supervision. Although recent studies use many features to prune wrong label ahead of training, they suffer from error propagation and bring much complexity. In this paper, we propose an end-to-end typing model, called the path-based attention neural model (PAN), to learn a noise-robust performance by leveraging the hierarchical structure of types. Experiments on two data sets demonstrate its effectiveness.

Original languageEnglish
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Pages8179-8180
Number of pages2
ISBN (Electronic)9781577358008
StatePublished - 2018
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: 2 Feb 20187 Feb 2018

Publication series

Name32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Conference

Conference32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Country/TerritoryUnited States
CityNew Orleans
Period2/02/187/02/18

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