A Method for Recognizing Entities in Power News Texts Based on Dependency Syntactic Parsing

Addressing the challenge that news texts in the power field often contain numerous professional terms and many new terms are generated every year, which are difficult to accurately identify using general named entity recognition methods, this paper proposes an entity recognition model for power text...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of advanced computational intelligence and intelligent informatics Jg. 29; H. 6; S. 1283 - 1291
Hauptverfasser: Wu, Yun, Liu, Xinru, Du, Yan, Yang, Jieming, Liu, Zhenhong, Yang, Kai, Wang, Ziyi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Tokyo Fuji Technology Press Co. Ltd 20.11.2025
Schlagworte:
Accuracy
Deep learning
Informatics
Natural language processing
Neural networks
Optimization
Recall
Recognition
Semantics
Texts
Words (language)
ISSN:1343-0130, 1883-8014
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Addressing the challenge that news texts in the power field often contain numerous professional terms and many new terms are generated every year, which are difficult to accurately identify using general named entity recognition methods, this paper proposes an entity recognition model for power texts based on dependency syntactic analysis (SYN-BiLSTM-CRF). This model first generates power text word vectors and inputs them into a forward LSTM for feature extraction. Simultaneously, dependency syntactic parsing is performed on the power text, and the syntactic information vectors are fused with the output of the forward LSTM before being input into a backward LSTM. This enhances the model’s ability to learn inter-word dependency relations by incorporating additional syntactic features. Finally, CRF is employed to obtain the predicted NER labels. The experiments demonstrate that the proposed SYN-BiLSTM-CRF model achieves an F1-score of 85.36% on power-related texts, representing a 2.78% improvement over the baseline BiLSTM-CRF model (82.58%). Additionally, it attains a recall of 89.06%, outperforming the BERT model’s recall (87.59%). These results prove that the proposed method significantly enhances entity recognition accuracy in this specialized domain.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2025.p1283