Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 5/2022

08.03.2021 | Special Issue on Multi-modal Information Learning and Analytics on Big Data

A joint model for entity and relation extraction based on BERT

verfasst von: Bo Qiao, Zhuoyang Zou, Yu Huang, Kui Fang, Xinghui Zhu, Yiming Chen

Erschienen in: Neural Computing and Applications | Ausgabe 5/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In recent years, as the knowledge graph has attained significant achievements in many specific fields, which has become one of the core driving forces for the development of the internet and artificial intelligence. However, there is no mature knowledge graph in the field of agriculture, so it is a great significance study on the construction technology of agricultural knowledge graph. Named entity recognition and relation extraction are key steps in the construction of knowledge graph. In this paper, based on the joint extraction model LSTM-LSTM-Bias brought in BERT pre-training language model to proposed a agricultural entity relationship joint extraction model BERT-BILSTM-LSTM which is applied to the standard data set NYT and self-built agricultural data set AgriRelation. Experimental results showed that the model can effectively extracted the relationship between agricultural entities and entities.
Vorheriger Artikel Classification of thermal image of clinical burn based on incremental reinforcement learning
Nächster Artikel Fast overlap removal for location-related representation elements

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Goldberg Y, Levy O (2014) word2vec explained: deriving mikolov et al.’s negative-sampling word-embedding method. arXiv:1402.3722
2.
Zheng S, Wang F, Bao H, Hao Y, Zhou P, Xu B (2017) Joint extraction of entities and relations based on a novel tagging scheme. arXiv:1706.05075
3.
Zhou Z, Shin J, Zhang L, Gurudu S, Gotway M, Liang J (2017) Fine-tuning convolutional neural networks for biomedical image analysis: actively and incrementally. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7340–7351
4.
Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805
5.
Bikel DM, Schwartz R, Weischedel RM (1999) An algorithm that learns what’s in a name. Mach Learn 34(1–3):211–231CrossRef
6.
Fu G, Luke K-K (2005) Chinese named entity recognition using lexicalized hmms. ACM SIGKDD Explor Newsl 7(1):19–25CrossRef
7.
Chieu HL, Ng HT (2002) Named entity recognition: a maximum entropy approach using global information. In: COLING 2002: the 19th international conference on computational linguistics
8.
Uchimoto K, Ma Q, Murata M, Ozaku H, Isahara H (2000),“amed entity extraction based on a maximum entropy model and transformation rules. In: Proceedings of the 38th annual meeting of the association for computational linguistics, pp 326–335
9.
Isozaki H, Kazawa H (2002) Efficient support vector classifiers for named entity recognition. In: COLING 2002: the 19th international conference on computational linguistics
10.
Chiu JP, Nichols E (2015) Named entity recognition with bidirectional lstm-cnns. arXiv:1511.08308
11.
Huang Z, Xu W, Yu K (2015) Bidirectional LSTM-CRF models for sequence tagging. arXiv:1508.01991
12.
Ma X, Hovy E (2016) End-to-end sequence labeling via bi-directional LSTM-CNNS-CRF. arXiv:1603.01354
13.
Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C (2016) Neural architectures for named entity recognition. arXiv:1603.01360
14.
Wu H, Lu L, Yu B (2019) Chinese named entity recognition based on transfer learning and bilstm-crf. Small Micro Comput Syst 40:1142–1147
15.
Mintz M, Bills S, Snow R, Jurafsky D (2009) Distant supervision for relation extraction without labeled data. In: 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: volume 2-volume 2. Association for Computational Linguistics, pp 1003–1011
16.
Zelenko D, Aone C, Richardella A (2003) Kernel methods for relation extraction. J Mach Learn Res 3(Feb):1083–1106MathSciNetMATH
17.
Zhou G, Zhang M, Ji D, Zhu Q (2007) Tree kernel-based relation extraction with context-sensitive structured parse tree information. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL), pp 728–736
18.
Yao L, Riedel S, McCallum A (2010) Collective cross-document relation extraction without labelled data. In: Proceedings of the 2010 conference on empirical methods in natural language processing. Association for Computational Linguistics, pp 1013–1023
19.
Zeng D, Liu K, Lai S, Zhou G, Zhao J (2014) Relation classification via convolutional deep neural network. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, pp 2335–2344
20.
Nguyen TH, Grishman R (2015) Relation extraction: perspective from convolutional neural networks. In: Proceedings of the 1st workshop on vector space modeling for natural language processing, pp 39–48
21.
dos Santos CN, Xiang B, Zhou B (2015) Classifying relations by ranking with convolutional neural networks. arXiv:1504.06580
22.
Socher R, Huval B, Manning CD, Ng AY (2012) Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, pp 1201–1211
23.
Zhou P, Shi W, Tian J, Qi Z, Li B, Hao H, Xu B (2016) Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: short papers), pp 207–212
24.
Lin C, Miller T, Dligach D, Amiri H, Bethard S, Savova G (2018) Self-training improves recurrent neural networks performance for temporal relation extraction. In: Proceedings of the ninth international workshop on health text mining and information analysis, pp 165–176
25.
Zhang Y, Qi P, Manning CD (2018) Graph convolution over pruned dependency trees improves relation extraction. arXiv:1809.10185
26.
Zhu H, Lin Y, Liu Z, Fu J, Chua T-S, Sun M (2019) Graph neural networks with generated parameters for relation extraction. arXiv:1902.00756
27.
Shi P, Lin J (2019) Simple bert models for relation extraction and semantic role labeling. arXiv:1904.05255
28.
Shen T, Wang D, Feng S, Zhang Y (2019) Bert-based denoising and reconstructing data of distant supervision for relation extraction. In: CCKS2019-shared task
29.
Li Q, Ji H (2014) Incremental joint extraction of entity mentions and relations. In: Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 1: long papers), vol 1, pp 402–412
30.
Miwa M, Sasaki Y (2014) Modeling joint entity and relation extraction with table representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1858–1869
31.
Miwa M, Bansal M (2016) End-to-end relation extraction using lstms on sequences and tree structures. arXiv:1601.00770
32.
Zheng S, Hao Y, Lu D, Bao H, Xu J, Hao H, Xu B (2017) Joint entity and relation extraction based on a hybrid neural network. Neurocomputing 257:59–66CrossRef
33.
Li F, Zhang M, Fu G, Ji D (2017) A neural joint model for entity and relation extraction from biomedical text. BMC Bioinform 18(1):198CrossRef
34.
Fang LM et al (1994) Agricultural thesaurus (the third volume). China Agriculture Press, Beijing, pp 191–192
Metadaten
Titel
A joint model for entity and relation extraction based on BERT
verfasst von
Bo Qiao
Zhuoyang Zou
Yu Huang
Kui Fang
Xinghui Zhu
Yiming Chen
Publikationsdatum
08.03.2021
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 5/2022
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-021-05815-z

Weitere Artikel der Ausgabe 5/2022

Neural Computing and Applications 5/2022 Zur Ausgabe

Original Article

A real-time approach to recognition of Turkish sign language by using convolutional neural networks

Special issue on Multi-modal Information Learning and Analytics on Big Data

Cross-domain recommendation based on latent factor alignment

Special Issue on Multi-modal Information Learning and Analytics on Big Data

Considering optimization of English grammar error correction based on neural network

Special Issue on Multi-modal Information Learning and Analytics on Big Data

Condition monitoring and life prediction of the turning tool based on extreme learning machine and transfer learning

Special Issue on Multi-modal Information Learning and Analytics on Big Data

Fast overlap removal for location-related representation elements

Original Article

A novel aspect-based sentiment classifier using whale optimized adaptive neural network

Premium Partner

    Bildnachweise