RTJTN: Relational Triplet Joint Tagging Network for Joint Entity and Relation Extraction

Extracting entities and relations from unstructured sentences is one of the most concerned tasks in the field of natural language processing. However, most existing works process entity and relation information in a certain order and suffer from the error iteration. In this paper, we introduce a rel...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2021, Vol.2021 (1), p.3447473-3447473
Main Authors: Yang, Zhenyu, Wang, Lei, Ma, Bo, Yang, Yating, Dong, Rui, Wang, Zhen
Format: Article
Language:English
Subjects:
Analysis
Computational linguistics
Datasets
Information processing
Iterative methods
Judgments
Language processing
Marking
Methods
Natural language interfaces
Natural language processing
Pipelines
Sentences
Triplets
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Summary:Extracting entities and relations from unstructured sentences is one of the most concerned tasks in the field of natural language processing. However, most existing works process entity and relation information in a certain order and suffer from the error iteration. In this paper, we introduce a relational triplet joint tagging network (RTJTN), which is divided into joint entities and relations tagging layer and relational triplet judgment layer. In the joint tagging layer, instead of extracting entity and relation separately, we propose a tagging method that allows the model to simultaneously extract entities and relations in unstructured sentences to prevent the error iteration; and, in order to solve the relation overlapping problem, we propose a relational triplet judgment network to judge the correct triples among the group of triples with the same relation in a sentence. In the experiment, we evaluate our network on the English public dataset NYT and the Chinese public datasets DuIE 2.0 and CMED. The F1 score of our model is improved by 1.1, 6.0, and 5.1 compared to the best baseline model on NYT, DuIE 2.0, and CMED datasets, respectively. In-depth analysis of the model’s performance on overlapping problems and sentence complexity problems shows that our model has different gains in all cases.
ISSN:1687-5265
1687-5273
DOI:10.1155/2021/3447473