Exploiting Noisy Data in Distant Supervision Relation Classification

Yang, Kaijia; He, Liang; Dai, Xinyu; Huang, Shujian; Chen, Jiajun

doi:10.18653/v1/n19-1325

Cited by 14 publications

(10 citation statements)

References 20 publications

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“…However, such instance bags may not exist in a general supervised setting. Reinforcement learning (Qin et al, 2018;Yang et al, 2019;Wang et al, 2020) and curricular learning (Jiang et al, 2018;Huang and Du, 2019) methods use a clean validation set to obtain an auxiliary model for noise filtering, while constructing a perfectly labeled validation set is expensive.…”

Section: Related Workmentioning

confidence: 99%

Learning from Noisy Labels for Entity-Centric Information Extraction

Zhou¹,

Chen²

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Recent information extraction approaches have relied on training deep neural models. However, such models can easily overfit noisy labels and suffer from performance degradation. While it is very costly to filter noisy labels in large learning resources, recent studies show that such labels take more training steps to be memorized and are more frequently forgotten than clean labels, therefore are identifiable in training. Motivated by such properties, we propose a simple co-regularization framework for entity-centric information extraction, which consists of several neural models with identical structures but different parameter initialization. These models are jointly optimized with the task-specific losses and are regularized to generate similar predictions based on an agreement loss, which prevents overfitting on noisy labels. Extensive experiments on two widely used but noisy benchmarks for information extraction, TACRED and CoNLL03, demonstrate the effectiveness of our framework. We release our code to the community for future research 1 .

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Section: Related Workmentioning

confidence: 99%

Learning from Noisy Labels for Entity-Centric Information Extraction

Zhou¹,

Chen²

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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“…e main disadvantage of these datasets is their noisy labels. ere are many approaches proposed to deal with the problem of noisy labels, such as multiple-instance learning [16][17][18], reinforcement learning [19][20][21], the use of knowledge base side information [22,23], and attention mechanism [24,25]. In the Persian language, FarsBase [5] especially uses a distantly supervised method to extract triples for the knowledge base.…”

Section: 1mentioning

confidence: 99%

PERLEX: A Bilingual Persian-English Gold Dataset for Relation Extraction

Asgari-Bidhendi

Nasser

Janfada

et al. 2021

Scientific Programming

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Relation extraction is the task of extracting semantic relations between entities in a sentence. It is an essential part of some natural language processing tasks such as information extraction, knowledge extraction, question answering, and knowledge base population. The main motivations of this research stem from a lack of a dataset for relation extraction in the Persian language as well as the necessity of extracting knowledge from the growing big data in the Persian language for different applications. In this paper, we present “PERLEX” as the first Persian dataset for relation extraction, which is an expert-translated version of the “SemEval-2010-Task-8” dataset. Moreover, this paper addresses Persian relation extraction utilizing state-of-the-art language-agnostic algorithms. We employ six different models for relation extraction on the proposed bilingual dataset, including a non-neural model (as the baseline), three neural models, and two deep learning models fed by multilingual BERT contextual word representations. The experiments result in the maximum F1-score of 77.66% (provided by BERTEM-MTB method) as the state of the art of relation extraction in the Persian language.

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“…The main disadvantage of these datasets is their noisy labels. The are many approaches proposed to deal with the problem of noisy labels such as multiple-instance learning [15,16,17], reinforcement learning [18,19,20], the use of knowledge base side information [21,22], and attention mechanism [23,24]. In the Persian language, FarsBase [5] especially uses a distant-supervised method to extract triples for the knowledge base.…”

Section: Datasetsmentioning

confidence: 99%

PERLEX: A Bilingual Persian-English Gold Dataset for Relation Extraction

Asgari-Bidhendi¹,

Nasser²,

Janfada³

et al. 2020

Preprint

View full text Add to dashboard Cite

Relation extraction is the task of extracting semantic relations between entities in a sentence. It is an essential part of some natural language processing tasks such as information extraction, knowledge extraction, and knowledge base population. The main motivations of this research stem from a lack of a dataset for relation extraction in the Persian language as well as the necessity of extracting knowledge from the growing big-data in the Persian language for different applications. In this paper, we present "PERLEX" as the first Persian dataset for relation extraction, which is an experttranslated version of the "Semeval-2010-Task-8" dataset. Moreover, this paper addresses Persian relation extraction utilizing state-of-the-art language-agnostic algorithms. We employ six different models for relation extraction on the proposed bilingual dataset, including a non-neural model (as the baseline), three neural models, and two deep learning models fed by multilingual-BERT contextual word representations. The experiments result in the maximum f-score 77.66% (provided by BERTEM-MTB method) as the state-of-the-art of relation extraction in the Persian language.

show abstract

Exploiting Noisy Data in Distant Supervision Relation Classification

Cited by 14 publications

References 20 publications

Learning from Noisy Labels for Entity-Centric Information Extraction

Learning from Noisy Labels for Entity-Centric Information Extraction

PERLEX: A Bilingual Persian-English Gold Dataset for Relation Extraction

PERLEX: A Bilingual Persian-English Gold Dataset for Relation Extraction

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