Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2022

Abstract

Fine-grained entity typing (FET) aims to assign proper semantic types to entity mentions according to their context, which is a fundamental task in various entity-leveraging applications. Current FET systems usually establish on large-scale weaklysupervised/distantly annotation data, which may contain abundant noise and thus severely hinder the performance of the FET task. Although previous studies have made great success in automatically identifying the noisy labels in FET, they usually rely on some auxiliary resources which may be unavailable in real-world applications (e.g., pre-defined hierarchical type structures, humanannotated subsets). In this paper, we propose a novel approach to automatically correct noisy labels for FET without external resources. Specifically, it first identifies the potentially noisy labels by estimating the posterior probability of a label being positive or negative according to the logits output by the model, and then relabel candidate noisy labels by training a robust model over the remaining clean labels. Experiments on two popular benchmarks prove the effectiveness of our method. Our source code can be obtained from https://github.com/CCIIPLab/DenoiseFET.

Keywords

Natural language processing, Named entities, Natural language processing: applications, Natural language processing: information retrieval and text mining

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 31st International Joint Conference on Artificial Intelligence, Vienna, Austria,2022 July 23-29

First Page

4317

Last Page

4323

Identifier

10.24963/ijcai.2022/599

Publisher

International Joint Conferences on Artificial Intelligence

City or Country

California

Additional URL

https://doi.org/10.24963/ijcai.2022/599

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