research-article

Distant Supervision for Relation Extraction via Noise Filtering

Authors:

Jie YangAuthors Info & Claims

ICMLC '21: Proceedings of the 2021 13th International Conference on Machine Learning and Computing

Pages 361 - 367

https://doi.org/10.1145/3457682.3457743

Published: 21 June 2021 Publication History

Abstract

As a widely used method in relation extraction at the present stage suggests, distant supervision is affected by label noise. The data noise is introduced artificially due to the theory and the performance of distant supervision will be restricted during the modeling process. To solve this problem on the sentence level, the task of relation extraction in our project is modeled with two parts: sentence selector and relation extractor. Sentence selector, based on the theory of reinforcement learning, processes the corpus in units of entity pairs. The training corpus is divided into three parts including selected sentences, discarded sentences, and unlabeled sentences. We try to obtain more semantic information of the training corpus by introducing the intra-class attention and inter-class similarity. To make the operation of filtering noise data more accurate, this model evaluates the predicted value produced by the relation extractor between the selected and discarded sentences in the sentence package. The result shows that the redesigned reinforcement learning algorithm WPR-RL in this study can significantly improve the deficiencies of the existing approach. At the same time, we also carry out a number of composite tests to discuss the impact of each improvement on the performance of the model.

References

[1]

Chinchor, Nancy, and Patricia Robinson. "MUC-7 named entity task definition." Proceedings of the 7th Conference on Message Understanding. Vol. 29. 1997.

[2]

Socher, Richard, "Semantic compositionality through recursive matrix-vector spaces." Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning. 2012.

[3]

Zeng, Daojian, "Relation classification via convolutional deep neural network." Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. 2014.

[4]

Zhou, Peng, "Attention-based bidirectional long short-term memory networks for relation classification." Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2016.

[5]

Bollegala, Danushka Tarupathi, Yutaka Matsuo, and Mitsuru Ishizuka. "Relational duality: Unsupervised extraction of semantic relations between entities on the web." Proceedings of the 19th international conference on World wide web. 2010.

[6]

Yao, Limin, Sebastian Riedel, and Andrew McCallum. "Unsupervised relation discovery with sense disambiguation." Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2012.

[7]

Mintz, Mike, "Distant supervision for relation extraction without labeled data." Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. 2009.

[8]

Riedel, Sebastian, Limin Yao, and Andrew McCallum. "Modeling relations and their mentions without labeled text." Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, Heidelberg, 2010.

[9]

Zeng, Daojian, "Distant supervision for relation extraction via piecewise convolutional neural networks." Proceedings of the 2015 conference on empirical methods in natural language processing. 2015.

[10]

Lin, Yankai, "Neural relation extraction with selective attention over instances." Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.

[11]

Ji, Guoliang, "Distant supervision for relation extraction with sentence-level attention and entity descriptions." AAAI. Vol. 3060. 2017.

[12]

Jat, Sharmistha, Siddhesh Khandelwal, and Partha Talukdar. "Improving distantly supervised relation extraction using word and entity based attention." arXiv preprint arXiv:1804.06987 (2018).

[13]

Wu, Shanchan, Kai Fan, and Qiong Zhang. "Improving distantly supervised relation extraction with neural noise converter and conditional optimal selector." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. 2019.

[14]

Ye, Zhi-Xiu, and Zhen-Hua Ling. "Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions." Proceedings of the ACL: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.

[15]

Yuan, Yujin, "Cross-relation cross-bag attention for distantly-supervised relation extraction." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. 2019.

[16]

Wu, Yi, David Bamman, and Stuart Russell. "Adversarial training for relation extraction." Proceedings of the EMNLP. 2017.

[17]

Shi, Ge, "Genre separation network with adversarial training for cross-genre relation extraction." Proceedings of the EMNLP. 2018.

[18]

Zeng, Xiangrong, "Large Scaled Relation Extraction With Reinforcement Learning." AAAI. 2018.

[19]

Feng, Jun, "Reinforcement learning for relation classification from noisy data." AAAI. 2018.

[20]

Qin, Pengda, W. Xu, and W. Y. Wang . "Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning." Meeting of the ACL 2018.

[21]

He, Zhengqiu, "Improving Neural Relation Extraction with Positive and Unlabeled Learning." AAAI. 2020.

[22]

Bordes, Antoine, "Translating embeddings for modeling multi-relational data." Advances in neural information processing systems 26 (2013): 2787-2795.

[23]

Yang, Kaijia, "Exploiting Noisy Data in Distant Supervision Relation Classification." Proceedings of the 2019 Conference of the ACL: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.

[24]

Sutton, Richard S., "Policy gradient methods for reinforcement learning with function approximation." Advances in neural information processing systems. 2000.

[25]

Williams, Ronald J. "Simple statistical gradient-following algorithms for connectionist reinforcement learning." Machine learning 8.3-4 (1992): 229-256.

[26]

Le, Quoc, and Tomas Mikolov. "Distributed representations of sentences and documents." International conference on machine learning. 2014.

[27]

Riedel, Sebastian, Limin Yao, and Andrew McCallum. "Modeling relations and their mentions without labeled text." Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, Heidelberg, 2010.

Cited By

Han PLiang GWang Y(2025)A Zero-Shot Framework for Low-Resource Relation Extraction via Distant Supervision and Large Language ModelsElectronics10.3390/electronics1403059314:3(593)Online publication date: 2-Feb-2025
https://doi.org/10.3390/electronics14030593
Long JYin ZHan YHuang W(2024)MKDAT: Multi-Level Knowledge Distillation with Adaptive Temperature for Distantly Supervised Relation ExtractionInformation10.3390/info1507038215:7(382)Online publication date: 30-Jun-2024
https://doi.org/10.3390/info15070382

Recommendations

Distant Supervision for Relation Extraction via Group Selection
ICONIP 2015: Proceeings, Part II, of the 22nd International Conference on Neural Information Processing - Volume 9490

Distant supervision DS aligns relations between name entities from a knowledge base KB with free text and automatically annotates the training corpus with relation mentions. One big challenge of DS is that the heuristically generated relation labels ...
Distant supervision for relation extraction with hierarchical attention-based networks
Abstract
Distant supervision employs external knowledge bases to automatically label corpora. The labeled sentences in a corpus are usually packaged and trained for relation extraction using a multi-instance learning paradigm. The automated ...
Highlights
- Propose a novel hierarchical attention-based networks for relation extraction.
- ...
Distant supervised relation extraction with position feature attention and selective bag attention
Abstract
Distant supervision greatly reduces manual consumption by automatically labeling data. The relation extraction methods under distant supervision divide sentences with the same entity pair into a bag, and perform training and testing on ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICMLC '21: Proceedings of the 2021 13th International Conference on Machine Learning and Computing

February 2021

601 pages

ISBN:9781450389310

DOI:10.1145/3457682

Copyright © 2021 ACM.

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 June 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

the National Natural Science Foundation of China

Conference

ICMLC 2021

ICMLC 2021: 2021 13th International Conference on Machine Learning and Computing

February 26 - March 1, 2021

Shenzhen, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
144
Total Downloads

Downloads (Last 12 months)43
Downloads (Last 6 weeks)0

Reflects downloads up to 02 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Han PLiang GWang Y(2025)A Zero-Shot Framework for Low-Resource Relation Extraction via Distant Supervision and Large Language ModelsElectronics10.3390/electronics1403059314:3(593)Online publication date: 2-Feb-2025
https://doi.org/10.3390/electronics14030593
Long JYin ZHan YHuang W(2024)MKDAT: Multi-Level Knowledge Distillation with Adaptive Temperature for Distantly Supervised Relation ExtractionInformation10.3390/info1507038215:7(382)Online publication date: 30-Jun-2024
https://doi.org/10.3390/info15070382

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten