Syntax-Enhanced Entity Relation Extraction with Complex Knowledge
Source abstract
Abstract
Entity relation extraction, a fundamental and essential task in natural language processing (NLP), has garnered significant attention over an extended period., aiming to extract the core of semantic knowledge from unstructure... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Natural Language Processing Techniques · Topic Modeling · Service-Oriented Architecture and Web Services
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (33)
Show source-provided references
1. A hierarchical framework for relation extraction with reinforcement learning
2. Mao · Advances in artificial intelligence big data and algorithms · 2023 · 663
3. Chen X, Li L, Qiao S, Zhang N, Tan C, Jiang Y, et al. One model for all domains: collaborative domain-prefix tuning for cross-domain NER. arXiv:2301.10410. 2023.10.24963/ijcai.2023/559 ↗
4. Ye R, Ge R, Fengting Y, Chai J, Wang Y, Chen S. Leveraging unstructured text data for federated instruction tuning of large language models. arXiv:2409.07136. 2024.10.1007/978-3-031-82240-7_9 ↗
5. Information extraction and named entity recognition supported social media sentiment analysis during the COVID-19 pandemic10.3390/app112211017 ↗
6. RoBGP: a Chinese nested biomedical named entity recognition model based on roberta and global pointer
7. A clinical named entity recognition model using pretrained word embedding and deep neural networks10.1016/j.dajour.2024.100426 ↗
8. A U-shaped network-based grid tagging model for Chinese named entity recognition
9. Graph convolutional networks embedding textual structure information for relation extraction
10. Deep learning-based relation extraction and knowledge graph-based representation of construction safety requirements10.1016/j.autcon.2022.104696 ↗
11. Incremental joint extraction of entity mentions and relations
12. Exploiting syntactico-semantic structures for relation extraction
13. Joint extraction of entities and overlapping relations using position-attentive sequence labeling
14. Jointly extracting multiple triplets with multilayer translation constraints
15. Zhong Z, Chen D. A frustratingly easy approach for joint entity and relation extraction. arXiv:2010.12812. 2020.10.18653/v1/2021.naacl-main.5 ↗
16. Joint entity recognition and relation extraction as a multi-head selection problem10.1016/j.eswa.2018.07.032 ↗
17. Wei Z, Su J, Wang Y, Tian Y, Chang Y. A novel cascade binary tagging framework for relational triple extraction. arXiv:1909.03227. 2019.10.18653/v1/2020.acl-main.136 ↗
18. Eberts M, Ulges A. Span-based joint entity and relation extraction with transformer pre-training. In: ECAI 2020. IOS Press; 2020. p. 2006–13.
19. Joint entity and relation extraction with set prediction networks10.1109/TNNLS.2023.3264735 ↗
20. Extracting relational facts by an end-to-end neural model with copy mechanism
21. Li X, Yin F, Sun Z, Li X, Yuan A, Chai D Entity relation extraction as multi-turn question answering. arXiv:1905.05529. 2019.10.18653/v1/P19-1129 ↗
22. Wang Y, Yu B, Zhang Y, Liu T, Zhu H, Sun L. TPLinker: single-stage joint extraction of entities and relations through token pair linking. arXiv:2010.13415. 2020.10.18653/v1/2020.coling-main.138 ↗
23. A relation-specific attention network for joint entity and relation extraction
24. Zheng S, Wang F, Bao H, Hao Y, Zhou P, Xu B. Joint extraction of entities and relations based on a novel tagging scheme. arXiv:1706.05075. 2017.10.18653/v1/P17-1113 ↗
25. Devlin J, Chang MW, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805. 2018.
26. Pre-training with whole word masking for chinese BERT10.1109/TASLP.2021.3124365 ↗
27. Construction and application of an ontology-based domain-specific knowledge graph for petroleum exploration and development10.1016/j.gsf.2022.101426 ↗
28. Modelling context and syntactical features for aspect-based sentiment analysis
29. Chinese semantic role labeling with bidirectional recurrent neural networks
30. Combining contextualized word representation and sub-document level analysis through Bi-LSTM+CRF architecture for clinical de-identification10.1016/j.knosys.2020.106649 ↗
31. Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C. Neural architectures for named entity recognition. arXiv:1603.01360. 2016.10.18653/v1/N16-1030 ↗
32. Joint entity and relation extraction from scientific documents: role of linguistic information and entity types
33. Machine reading comprehension combined with semantic dependency for Chinese zero pronoun resolution10.1007/s10462-022-10364-5 ↗