A comprehensive survey on relation extraction: recent advances and new frontiers
Open accessAbstract
Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as knowledge graph completion and question answering. In recent years, deep neural networks have dominated the field of RE and made noticeable progress. Subsequently, the large pre-trained language models (PLMs) have taken the state-of-the-art RE to a new level. This survey provides a comprehensive review of existing deep learning techniques for RE. First, we introduce RE resources, including datasets and evaluation metrics. Second, we propose a new taxonomy to categorize existing works from three perspectives, i.e., text representation, context encoding, and triplet prediction. Third, we discuss several important challenges faced by RE and summarize potential techniques to tackle these challenges. Finally, we outline some promising future directions and prospects in this field. This survey is expected to facilitate researchers’ collaborative efforts to address the challenges of real-world RE systems.
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LLMs have advantages including support for information extraction and synthesis, fact checking, multilingual analysis, and decision-support assistants, but also important limitations in uncertainty handling, self-assessment, and metacognitive reliability in high-stakes settings.
“While acknowledging various advantages of LLMs (Choi and Ferrara, 2024; Zhao et al., 2024), including their support for information extraction and synthesis, facts checking, multilingual analysis, and decision-support assistants, recent work (Griot et al., 2025) has also highlighted their important limitations in uncertainty handling, self-assessment, and metacognitive reliability in high-stakes”
From Enhancement of modern defense capabilities by convergence of cognitive neuroscience and artificial intelligence (2026) · cites this paper as Zhao et al., 2024
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Bidirectional Recurrent Convolutional Neural Network for Relation Classification
Cai, R., Zhang, X., & Wang, H. (2016) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/p16-1072 · Cited by 226 · cited 2× · see citing works
“The F1 score, being the harmonic mean of precision and recall, offers a balanced reflection of the system’s performance.”
Xu, B., Wang, Q., Lyu, Y., Zhu, Y. T., & Mao, Z. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i16.17665 · Cited by 202 · see citing works
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification
Schick, T., Schmid, H., & Schütze, H. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.488 · Cited by 184 · see citing works
SciREX: A Challenge Dataset for Document-Level Information Extraction
Jain, S., van Zuylen, M., Hajishirzi, H., & Beltagy, I. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.670 · Cited by 159 · see citing works
Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning
Zeng, X., He, S., Zeng, D., Liu, K., Liu, S., & Zhao, J. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1035 · Cited by 150 · cited 2× · see citing works
“For each instance, the RE model generates a ranked list of 𝐾 predictions, prioritizing them based on certain relevance criteria.”
Packed Levitated Marker for Entity and Relation Extraction
Ye, D., Lin, Y., Li, P., & Sun, M. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.337 · Cited by 144 · see citing works
Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again
Gutiérrez, B. J., McNeal, N., Washington, C., Chen, Y., Li, L., Sun, H., & Su, Y. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.329 · Cited by 141 · see citing works
Zhao, K., Xu, H., Cheng, Y., Li, X., & Gao, K. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106888 · Cited by 140 · see citing works
Proceedings of the 11th International Conference on Computational Semantics
Hunter, J., Asher, N. M., & Lascarides, A. (2015) · The Association for Computational Linguistics · Cited by 119 · see citing works
Document-Level Relation Extraction with Reconstruction
Wang, X., Chen, K., & Zhao, T. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i16.17667 · Cited by 118 · see citing works
Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction
Sainz, O., de Lacalle, O. L., Labaka, G., Barrena, A., & Agirre, E. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.92 · Cited by 110 · cited 1× · see citing works
“Roy and Pan [127] incorporated entity-level KG into pre-trained BERT for clinical RE, integrating the medical knowledge by several techniques.”
A benchmark and comprehensive survey on knowledge graph entity alignment via representation learning
Zhang, R., Trisedya, B. D., Li, M., Jiang, Y., & Qi, J. (2022) · The VLDB Journal · doi:10.1007/s00778-022-00747-z · Cited by 95 · see citing works
SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction
Zeng, S., Wu, Y., & Chang, B. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.47 · Cited by 86 · see citing works
Li, Y., Long, G., Shen, T., Zhou, T., Yao, L., Huo, H., & Jiang, J. B. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6342 · Cited by 84 · see citing works
Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction
Du, J., Han, J., Way, A., & Wan, D. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1245 · Cited by 82 · cited 2× · see citing works
“The main drawbacks of PLMs are their resource-intensive nature, requiring significant computational power for both training and inference, and their tendency to overfit on smaller or domain-specific datasets.”
Yuan, L., Cai, Y., Wang, J., & Li, Q. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i9.26309 · Cited by 78 · see citing works
UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction
Tang, W., Xu, B., Zhao, Y., Mao, Z., Liu, Y., Liao, Y., & Xie, H. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.477 · Cited by 78 · see citing works
Cross-lingual Structure Transfer for Relation and Event Extraction
Subburathinam, A., Lu, D., Ji, H., May, J., Chang, S.-F., Sil, A., & Voss, C. R. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1030 · Cited by 77 · cited 1× · see citing works
“Therefore, some works have been proposed to improve cross-lingual transfer for RE, including utilizing universal dependency structure parses [146] and mBERT [122].”
A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction
Shen, Y., Ma, X., Tang, Y., & Lü, W. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449895 · Cited by 77 · see citing works
Cross-Relation Cross-Bag Attention for Distantly-Supervised Relation Extraction
Yuan, Y., Liu, L., Tang, S., Zhang, Z. M., Zhuang, Y., Pu, S., Wu, F., & Ren, X. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301419 · Cited by 73 · see citing works
Fine-Grained Temporal Relation Extraction
Vashishtha, S., Durme, B. V., & White, A. (2019) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1280 · Cited by 71 · cited 1× · see citing works
“(b) [161], a timeline illustrates the fine-grained (real-valued) temporal relations implicated in the text, mapping the temporal relations and event durations to real-valued scales.”
TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations
Li, X., Luo, X., Dong, C., Yang, D., Luan, B., & He, Z. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.635 · Cited by 67 · see citing works
Ji, B., Yu, J., Li, S., Ma, J., Wu, Q., Tan, Y., & Liu, H. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.8 · Cited by 66 · cited 1× · see citing works
“Precision@ 𝐾 measures the proportion of correctly identified relations among the top-𝐾 predictions made by the system.”
Deep Neural Approaches to Relation Triplets Extraction: a Comprehensive Survey
Nayak, T. K., Majumder, N., Goyal, P., & Poria, S. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09917-7 · Cited by 65 · see citing works
Chemical-induced disease relation extraction with various linguistic features
Gu, J., Qian, L., & Zhou, G. (2016) · Database · doi:10.1093/database/baw042 · Cited by 61 · cited 1× · see citing works
“In this section, we discuss recent RE methods tailored to different specific domains, including biomedical [31, 53, 88], financial [163], and legal [5], and scientific domains.”
Relation Extraction from Clinical Narratives Using Pre-trained Language Models.
Wei, Q., Ji, Z., Si, Y., Du, J., Wang, J., Tiryaki, F., Wu, S., Tao, C., Roberts, K. E., & Xu, H. (2019) · PubMed Central · Cited by 60 · see citing works
Choi, S.-P. (2016) · Journal of Information Science · doi:10.1177/0165551516673485 · Cited by 59 · cited 1× · see citing works
“In this section, we discuss recent RE methods tailored to different specific domains, including biomedical [31, 53, 88], financial [163], and legal [5], and scientific domains.”
Enhance Prototypical Network with Text Descriptions for Few-shot Relation Classification
Yang, K., Zheng, N., Dai, X., He, L., Huang, S., & Chen, J. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3412153 · Cited by 59 · cited 1× · see citing works
“According to the data structure, external knowledge can be divided into (1) unstructured text spans, including the descriptions of entity and relation, and (2) a structured knowledge graph (KG).”
Dialogue Relation Extraction with Document-Level Heterogeneous Graph Attention Networks
Chen, H., Hong, P., Han, W., Majumder, N., & Poria, S. (2023) · Cognitive Computation · doi:10.1007/s12559-023-10110-1 · Cited by 56 · cited 1× · see citing works
“Chen et al. [22] deployed a token graph attention network.”
Named Entity and Relation Extraction with Multi-Modal Retrieval
Wang, X., Cai, J., Jiang, Y., Xie, P., Tu, K., & Lü, W. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.437 · Cited by 54 · see citing works
Heterogeneous Graph Transformer for Graph-to-Sequence Learning
Yao, S., Wang, T., & Wan, X. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.640 · Cited by 53 · cited 1× · see citing works
“Yao et al. [204] proposed a heterogeneous graph transformer to model the different relations among individual subgraphs, including direct, indirect, and possible relations between nodes.”
A Survey on Relation Extraction
Cui, M., Li, L., Wang, Z., & You, M. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-7359-5_6 · Cited by 52 · see citing works
Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction
Zhang, R. H., Liu, Q., Fan, A. X., Ji, H., Zeng, D., Cheng, F., Kawahara, D., & Kurohashi, S. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.23 · Cited by 52 · see citing works
KPI-BERT: A Joint Named Entity Recognition and Relation Extraction Model for Financial Reports
Hillebrand, L., Deußer, T., Dilmaghani, T., Kliem, B., Loitz, R., Bauckhage, C., & Sifa, R. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956191 · Cited by 47 · cited 3× · see citing works
“In the financial domain, the RE systems focus on identifying specific relations within financial texts, such as automatically extracting and linking key performance indicators (KPIs) from financial documents [65].”
Consistent Representation Learning for Continual Relation Extraction
Zhao, K., Xu, H., Yang, J., & Gao, K. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.268 · Cited by 46 · see citing works
Relation Extraction From Biomedical and Clinical Text: Unified Multitask Learning Framework
Yadav, S., Jayashree, S. R., Saha, S., & Ekbal, A. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.3020016 · Cited by 43 · cited 1× · see citing works
“Yadav et al. [196] proposed a multi-task learning framework for relation extraction in biomedical and clinical domains, modeling the RE task with three subtasks to better utilize the shared representation.”
Joint Extraction of Multiple Relations and Entities by Using a Hybrid Neural Network
Zhou, P., Zheng, S., Xu, J., Qi, Z., Bao, H., & Xu, B. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-69005-6_12 · Cited by 39 · cited 2× · see citing works
“The performance of supervised learning RE systems is typically measured by comparing the predicted labels to the corresponding ground-truth annotations.”
Are Noisy Sentences Useless for Distant Supervised Relation Extraction?
Shang, Y.-M., Huang, H., Mao, X.-L., Sun, X., & Wei, W. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6407 · Cited by 39 · see citing works
Exploring Privileged Features for Relation Extraction with Contrastive Student-Teacher Learning
Xiaoyan, Z., Yang, M., Qu, Q., Xu, R., & Li, J. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3161584 · Cited by 39 · cited 2× · see citing works
“For each instance, the RE model generates a ranked list of 𝐾 predictions, prioritizing them based on certain relevance criteria.”
Let’s Stop Incorrect Comparisons in End-to-end Relation Extraction!
Taillé, B., Guigue, V., Scoutheeten, G., & Gallinari, P. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.301 · Cited by 38 · see citing works
MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction
Dong, M., Pan, C., & Luo, Z. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.212 · Cited by 37 · cited 1× · see citing works
“The prototype representations learned by specifical prototype-aware regularization can inject the implicit correlations between entities and relations.”
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations
Sun, K., Zhang, R., Mensah, S., Mao, Y., & Liu, X. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.304 · Cited by 36 · see citing works
Sun, Q., Huang, K., Yang, X., Tong, R., Zhang, K., & Poria, S. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645678 · Cited by 35 · cited 1× · see citing works
“What’s more, recent advances [75, 150, 190] in large language models (LLMs), such as GPT-3 [16], ChatGPT and GPT-4 6 [1], have demonstrated their exceptional performance across various natural language processing (NLP) tasks.”
Zhao, D., Wang, J., Lin, H., Wang, X., Yang, Z., & Zhang, Y. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107230 · Cited by 34 · see citing works
Yuan, C., Huang, H., Feng, C., Liu, X., & Wei, X. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017418 · Cited by 32 · see citing works
A Relation-Oriented Clustering Method for Open Relation Extraction
Zhao, J., Gui, T., Zhang, Q., & Zhou, Y. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.765 · Cited by 28 · cited 2× · see citing works
“Another type is the clustering-based unsupervised relation discovery method [236]”
GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models
Jiang, P., Lin, J., Wang, Z., Sun, J., & Han, J. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-long.155 · Cited by 28 · see citing works
In Layman’s Terms: Semi-Open Relation Extraction from Scientific Texts
Kruiper, R., Vincent, J. F. V., Chen-Burger, Y.-H., Desmulliez, M. P. Y., & Konstas, I. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.137 · Cited by 27 · see citing works
FinRED: A Dataset for Relation Extraction in Financial Domain
Sharma, S., Nayak, T., Bose, A., Meena, A. K., Dasgupta, K., Ganguly, N., & Goyal, P. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3487553.3524637 · Cited by 27 · see citing works
Biomedical Information Retrieval incorporating Knowledge Graph for Explainable Precision Medicine
Yang, Z. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401458 · Cited by 26 · cited 1× · see citing works
“It can be used for many downstream applications [111, 187], such as knowledge graph completion [23] and alignment [230], question answering [100], and information retrieval [203].”
An Embarrassingly Simple Model for Dialogue Relation Extraction
Xue, F., Sun, A., Zhang, H., Ni, J., & Chng, E. S. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747486 · Cited by 25 · cited 1× · see citing works
“Xue et al. [192] took a novel input format and utilized a BERT-based model to capture the interrelations among entity pairs.”
Thillaisundaram, A., & Togia, T. (2019) · Workshop on BioNLP Open Shared Tasks (BioNLP-OST) · doi:10.18653/v1/d19-5713 · Cited by 22 · cited 1× · see citing works
“Thillaisundaram and Togia [154] proposed extracting biomedical triplets with an extended BERT model, which encoded gene-disease pairs and their textual context to predict the “function change” relation.”
KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents
Deußer, T., Ali, S. M., Hillebrand, L., Nurchalifah, D., Jacob, B., Bauckhage, C., & Sifa, R. (2022) · International Conference on Machine Learning and Applications · doi:10.1109/icmla55696.2022.00254 · Cited by 22 · cited 1× · see citing works
“For example, Deußer et al. [37] explored extracting KPIs from financial documents where a word-level weighting scheme models the inherently fuzzy borders of the entity pairs and the corresponding relations.”
Wang, Y., Liu, X., Hu, W., & Zhang, T. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.276 · Cited by 22 · see citing works
CFSRE: Context-aware based on frame-semantics for distantly supervised relation extraction
Zhao, H., Li, R., Li, X., & Tan, H. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106480 · Cited by 20 · see citing works
Learning from Unlabelled Data for Clinical Semantic Textual Similarity
Wang, Y., Verspoor, K. M., & Baldwin, T. (2020) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2020.clinicalnlp-1.25 · Cited by 20 · cited 1× · see citing works
“In the biomedical field, the RE models aim to automatically extract relations between biomedical entities (proteins, genes, diseases, etc.) from a rich source of biomedical texts [126, 170].”
Sai, S. T. Y. S., Chakraborty, P., Dutta, S., Sanyal, D. K., & Das, P. P. (2021) · Cited by 20 · see citing works
Kanjirangat, V., & Rinaldi, F. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103893 · Cited by 18 · cited 1× · see citing works
“Kanjirangat and Rinaldi [79] proposed a distantly supervised biomedical RE method using the shortest dependency path for selecting representative samples.”
Phi, V.-T., Santoso, J., Tran, V.-H., Shindo, H., Shimbo, M., & Matsumoto, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2932041 · Cited by 17 · see citing works
A Dynamic Parameter Enhanced Network for distant supervised relation extraction
Gou, Y., Lei, Y., Liu, L., Zhang, P., & Peng, X. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105912 · Cited by 17 · see citing works
Yu, E., Han, W., Tian, Y., & Chang, Y. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.146 · Cited by 16 · cited 1× · see citing works
“Additionally, some works [180, 218] that consider both sentence-level and bag-level information simultaneously, explore explicit cross-level interactions to further improve the performance of DSRE.”
Wang, Z., Song, H., Ren, Z., Ren, P., Chen, Z., Liu, X., Li, H., & de Rijke, M. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462873 · Cited by 15 · cited 1× · see citing works
“Wang et al. [173] focused on cross-domain contract element extraction and proposed a Bi-FLEET model, which incorporates a clause-element relation encoder with a bi-directional feedback scheme.”
Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study
Xu, X., Chen, X., Zhang, N., Xie, X., Chen, X., & Chen, H. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.29 · Cited by 12 · see citing works
Few-Shot Relation Extraction With Automatically Generated Prompts
Zhao, X., Yang, M., Qu, Q., & Xu, R. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3365858 · Cited by 12 · cited 1× · see citing works
“Some approaches [49, 214, 215, 241] also introduce external information to compensate for the limited information in FSRE.”
Cross-sentence N-ary Relation Extraction using Entity Link and Discourse Relation
Lee, S., Seo, S., Oh, B., Lee, K.-H., Shin, D.-H., & Lee, Y. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3412011 · Cited by 11 · cited 1× · see citing works
“For example, detecting the relationship between a drug, a cancer patient, and a specific gene mutation is crucial for determining whether a drug is relevant for treating cancer patients with a certain mutation in a given gene.”
Learning Relation Ties with a Force-Directed Graph in Distant Supervised Relation Extraction
Shang, Y.-M., Huang, H., Sun, X., Wei, W., & Mao, X.-L. (2022) · ACM Transactions on Information Systems · doi:10.1145/3520082 · Cited by 10 · see citing works
Creating A Large-Scale Financial News Corpus for Relation Extraction
Wu, H., Lei, Q., Zhang, X., & Luo, Z. (2020) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd49809.2020.9137442 · Cited by 10 · cited 1× · see citing works
“Wu et al. [179] focused on Chinese financial entity recognition and relation extraction and proposed a mixed pattern with POS tagging is proposed to generate the quadruples (entity1, entity2, relation, text) from the unstructured finical text.”
D-REX: Dialogue Relation Extraction with Explanations
Albalak, A., Embar, V. R., Tuan, Y.-L., Getoor, L. C., & Wang, W. Y. (2022) · Workshop on NLP for Conversational AI (NLP4ConvAI) · doi:10.18653/v1/2022.nlp4convai-1.4 · Cited by 8 · see citing works
ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction
Xu, W., Deng, Y., Lei, W., Zhao, W., Chua, T., & Lam, W. P. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.166 · Cited by 7 · see citing works
Iterative Document-level Information Extraction via Imitation Learning
Chen, Y., Gantt, W., Gu, W., Chen, T., White, A. S., & Durme, B. V. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.136 · Cited by 7 · see citing works
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A Survey of the Real-Time Metaverse: Challenges and Opportunities
Hatami, M., Qu, Q., Chen, Y., Kholidy, H. A., Blasch, E., & Ardiles-Cruz, E. (2024) · Future Internet · doi:10.3390/fi16100379 · Cited by 108 · see citing works
Deep Learning in Multimodal Fusion for Sustainable Plant Care: A Comprehensive Review
Yang, Z., Li, Y., Wang, R.-F., Hu, P., & Su, W. (2025) · Sustainability · doi:10.3390/su17125255 · Cited by 83 · see citing works
Gao, X., Gao, J., & Qureshi, W. A. (2025) · Agronomy · doi:10.3390/agronomy15081954 · Cited by 51 · see citing works
Generative AI vs. humans in online hotel review management: A Task-Technology Fit perspective
Zhang, H., Xiang, Z., & Zach, F. (2025) · Tourism Management · doi:10.1016/j.tourman.2025.105187 · Cited by 57 · see citing works
AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
Yang, Z., Zhang, Y., Zeng, J., Yang, Y., Jia, Y., Song, H., Lv, T., Sun, Q., & An, J. (2025) · Drones · doi:10.3390/drones9060392 · Cited by 49 · see citing works
Multimodal fusion and vision–language models: A survey for robot vision
Han, X., Chen, S., Fu, Z., Feng, Z., Fan, L., An, D., Wang, C., Guo, L., Meng, W., Zhang, X., et al. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103652 · Cited by 41 · see citing works
“It combines various sources of information, such as vision, language, depth data, lidar, and speech, to enhance the capability of scene semantic understanding [39, 40, 41].”
Survey on legal information extraction: current status and open challenges
Premasiri, D., Ranasinghe, T., Mitkov, R., El‐Haj, M., & Frommholz, I. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02600-5 · Cited by 22 · see citing works
Liang, S. (2024) · Applied Artificial Intelligence · doi:10.1080/08839514.2024.2419609 · Cited by 13 · see citing works
Hải, N. N., Nguyen, T.-T., & Ngo, Q. T. (2025) · Journal of risk and financial management · doi:10.3390/jrfm18080417 · Cited by 14 · see citing works
Lei, X., Wang, Y., Zhu, Z., Dai, O., Chen, S., Wen, C., & Pang, J. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.105333 · Cited by 12 · see citing works
Few-Shot Relation Extraction With Automatically Generated Prompts
Zhao, X., Yang, M., Qu, Q., & Xu, R. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3365858 · Cited by 12 · see citing works
Zhang, Y., Gao, T., Xie, H., Liu, H., Ge, M., Xu, B., Zhu, N., & Lu, Z. (2025) · Remote Sensing · doi:10.3390/rs17040641 · Cited by 11 · see citing works
Research on a Short-Term Power Load Forecasting Method Based on a Three-Channel LSTM-CNN
Xiaojing, Z., Peng, H., Zhang, L., & Ma, H. (2025) · Electronics · doi:10.3390/electronics14112262 · Cited by 11 · see citing works
Liquid neural network in smart manufacturing: A new opportunity
Leng, J., Zhu, T., Li, J., Wang, B., Yang, B. Y., Li, X., Liu, Q., Chen, X., & Wang, L. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.104222 · Cited by 10 · see citing works
Using large language models for semantic interoperability: A systematic literature review
Abu-Salih, B., Alotaibi, S., Alanazi, A. L., Abukhurma, R., Al-Shboul, B. A., Khoury, A., & Aljaafari, M. A. H. (2025) · ICT Express · doi:10.1016/j.icte.2025.06.011 · Cited by 14 · see citing works
Language models for drug–drug interactions: current applications, pitfalls, and future directions
Meslamani, A. Z. A., & Hajal, A. A. (2025) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1080/17425255.2025.2551724 · Cited by 12 · see citing works
“These approaches include entity recognition, relation classification, contextual understanding, zero-shot/few-shot learning, knowledge augmentation, joint task learning, and explainability features [22,43,66,72,73].”
Chen, L., Shang, S., & Wang, Y. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2851 · Cited by 11 · see citing works
Delmas, M., Wysocka, M., & Freitas, A. (2024) · Computational Linguistics · doi:10.1162/coli_a_00520 · Cited by 10 · see citing works
Lu, L., Wang, Y., & Zhou, X. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf599 · Cited by 8 · see citing works
“These methods have demonstrated significant success in fields such as image processing and natural language processing [23, 24].”
AgriFusion: Multiscale RGB–NIR Fusion for Semantic Segmentation in Airborne Agricultural Imagery
Li, X., Qiao, L., & Yang, C. (2025) · AgriEngineering · doi:10.3390/agriengineering7110388 · Cited by 8 · see citing works
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Combining language models for knowledge extraction from Italian TEI editions
Santini, C. (2024) · Frontiers in Computer Science · doi:10.3389/fcomp.2024.1472512 · Cited by 6 · see citing works
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A multimodal surrogate model for the multiphysics optimization of dual-rotor in-wheel motors
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Ethical issues and coping strategies for artificial intelligence in medical research
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Emotionally based strategic communications as a new tool in defensive cognitive warfare
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A survey of Few-Shot Relation Extraction combining meta-learning with prompt learning
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Advances in the machine learning construction process for citrus quality and disease detection
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Bearing Anomaly Detection Method Based on Multimodal Fusion and Self-Adversarial Learning
Liu, H. W., Qin, Y., & Tu, D. (2026) · Sensors · doi:10.3390/s26020629 · Cited by 3 · see citing works
“This study adopts feature-level fusion [20], as it best aligns with the nature of our data.”
A self-modified hypergraph neural network for multimodal relation extraction
Xu, M., Zhong, H., Liu, J., Song, L., Li, Y., & Shi, L. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104673 · Cited by 3 · see citing works
Relational Graph-Bridged Image-Text Interaction: A Novel Method for Multi-Modal Relation Extraction
Zheng, Z., He, T., Liu, M., Wang, Z., Fu, R., & Qin, B. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448507 · Cited by 3 · see citing works
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“While LLMs have made noticeable strides in capturing multiple relationships, there are still challenges to address such as the effectiveness, scalability, and reliability of these methods in constructing detailed and accurate knowledge graphs [15].”
Cross-Lingual Short-Text Semantic Similarity for Kannada–English Language Pair
N., M. S., Holla, K. R., Harivinod, N., & Ganiga, R. (2024) · Computers · doi:10.3390/computers13090236 · Cited by 3 · see citing works
Corporate relation extraction for the construction of knowledge-bases against tax fraud
López-Gazpio, I., Baselga-Pascual, L., & Garmendia-Lazcano, A. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113026 · Cited by 3 · see citing works
SDZRE: A semantic distillation method for zero-shot relation extraction
Zhou, Y.-J., Lu, L., Li, H., Liu, X.-Y., Huang, D., & Chen, Y. (2025) · Expert systems with applications · doi:10.1016/j.eswa.2025.127609 · Cited by 2 · see citing works
How LLMs Aid in Domain Modeling: Opportunities and Challenges
Shi, H., Liu, S., & Pan, L. (2025) · IEEE International Conference on Software Services Engineering (SSE) · doi:10.1109/sse67621.2025.00033 · Cited by 2 · see citing works
“Numerous studies have focused on applying LLMs to information extraction (IE) tasks [14], [15].”
Multilingual Relation Extraction: A Survey
Ali, M., Speck, R., Zahera, H. M., Saleem, M., Moussallem, D., & Ngomo, A.-C. N. (2025) · IEEE Access · doi:10.1109/access.2025.3604258 · Cited by 2 · see citing works
“Engaging local linguistic communities not only aids in data collection but also ensures that the nuances of each language are accurately captured [20], [119].”
Seismic Facies Recognition Based on Multimodal Network with Knowledge Graph
Yan, B., Li, M., Pan, R., & Zhao, J. (2025) · Applied Sciences · doi:10.3390/app152011087 · Cited by 3 · see citing works
Li, L., Li, H., Fang, J., Wu, D., Ali, W., Jiang, M., & Yan, J. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108903 · Cited by 2 · see citing works
L3X: Long Object List Extraction from Long Documents
Singhania, S., Razniewski, S., & Weikum, G. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761460 · Cited by 2 · see citing works
Document-level relation extraction using context-aware entity representation
Nilesh, N., Gupta, A. K., & Pandey, A. C. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130408 · Cited by 2 · see citing works
Rethinking Convolutional Neural Network in Multimodal Sequential Recommendation
Zhou, Z., Meng, X., & Zhang, Y. (2025) · ACM Transactions on Information Systems · doi:10.1145/3777377 · Cited by 2 · see citing works
Mapari, R. B., Tamboli, A. I., Tamhan, P., Shelar, Y., Bhise, P. S., & Kumar, C. A. (2025) · Indian Journal of Tuberculosis · doi:10.1016/j.ijtb.2025.11.012 · Cited by 2 · see citing works
A review of recent advances in generative artificial intelligence models for biomolecular sciences
Jiang, J., Li, D., Wang, G., Hayes, N., Shi, Y.-Z., Qiu, H., Zhang, B., Zhou, T., & Wei, G. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.12.012 · Cited by 2 · see citing works
“These models capture relationships between data sources, facilitating data fusion and overcoming the limitations of single-modal data 98 .”
Logical Rule-Constrained Large Language Models for Document-Level Relation Extraction
Zhang, K., Wu, P., Yu, B., Li, Y., Kong, D., Zan, H., Peng, M., & Song, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3343-5_11 · Cited by 2 · see citing works
Zhao, Z., Cao, W., Fang, L., Chen, M., Liu, X., Qiu, J., Ma, L., Dong, W., & Zhao, X. (2025) · Journal of Food Composition and Analysis · doi:10.1016/j.jfca.2025.108848 · Cited by 2 · see citing works
Geometry-aware multimodal fusion for large-scale 3D scene understanding
Wang, Y., Zuo, Y., yi, T., Hong, X., Wu, J., & Bian, Z. (2026) · Optics Express · doi:10.1364/oe.585727 · Cited by 2 · see citing works
Wang, K., Li, Y., Wang, J., Zhang, Z., Zhou, S., & Zhou, R. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112257 · Cited by 2 · see citing works
Qi, C., Zheng, L., Wei, Y., Xu, H., Chen, P., & Gu, X. (2024) · Applied Sciences · doi:10.3390/app15010154 · Cited by 2 · see citing works
“The development of educational dialogue classification in low-resource contexts shares similarities with the evolution of relation extraction (RE) [11].”
Ge, Q., Wei, C., & Wu, D. (2025) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126625501956 · Cited by 2 · see citing works
Extraction-Augmented Generation of Scientific Abstracts Using Knowledge Graphs
Wang, H., & Lepage, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3551756 · Cited by 2 · see citing works
“At the micro level, the relations between entities are identified based on context within a document [37], [47], which can further be divided into document-level [27], [45] and sentence-level [29] identification.”
Mishra, P., Narayanasamy, S. K., & Srinivasan, K. (2025) · IEEE Access · doi:10.1109/access.2025.3559548 · Cited by 2 · see citing works
“The authors [25], [26] have discussed about the pressing challenges in exploring the ontology model to best suit for the climate change datasets and look for the possible approaches to store the extracted relations in the graph databases.”
Cosic, K., Kuljanac, A., & Palermo, S. (2026) · Frontiers in psychology · doi:10.3389/fpsyg.2026.1821242 · Cited by 3 · see citing works
“While acknowledging various advantages of LLMs (Choi and Ferrara, 2024; Zhao et al., 2024), including their support for information extraction and synthesis, facts checking, multilingual analysis, and decision-support assistants, recent work (Griot et al., 2025) has also highlighted their important…”
Veerabhadrappa, R., & Poornima, A. S. (2025) · Computer Systems Science and Engineering · doi:10.32604/csse.2025.061285 · Cited by 1 · see citing works
Che, S., He, Q., Yang, Z., Li, Y., & Du, N. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00035-0 · Cited by 1 · see citing works
“While sentence-level RE (Zhao et al. 2024) focuses on single sentences, document-level RE presented distinct challenges in the design of model architectures.”
A Joint Entity and Relation Extraction Model Driven by Fine-Grained Feature Extraction
Yu, H., Cao, H., Dong, C., Wang, H., Liang, H., & Cui, Y. (2025) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001425540096 · Cited by 1 · see citing works
A Syntax-Aware Graph Network with Contrastive Learning for Threat Intelligence Triple Extraction
He, Z., Zhao, Z., & Liu, Z. (2025) · Symmetry · doi:10.3390/sym17071013 · Cited by 1 · see citing works
Deng, W., Hu, D., Jiang, Z., Zhang, P., & Shi, Y. (2025) · Systems · doi:10.3390/systems13080682 · Cited by 1 · see citing works
Enhancing sparse triplet overlapping relation extraction using triaxial syntactic fusion approach
Wang, H., Tao, R., Fang, X., Liu, G., & Qin, K. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114387 · Cited by 1 · see citing works
Alsaadi, G. A., Alzanbaqi, S. F., Alharbi, S. A., Alahmadi, L. S., Alsaadi, R. Y., Alsaedi, D. A., Bashool, R. A., & Mohaini, M. A. (2025) · Journal of Advanced Trends in Medical Research · doi:10.4103/atmr.atmr_77_25 · Cited by 1 · see citing works
Fusing multiple reasoning tasks for event causality identification via prompt distillation
Zeng, Z., Si, Y., Cheng, Q., & Liu, Z. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130284 · Cited by 1 · see citing works
Cao, A., Tan, S., Xiao, P., Li, Q., Li, W., & Zhang, Z. (2025) · Ultrasonics · doi:10.1016/j.ultras.2025.107921 · Cited by 1 · see citing works
Junqi, B. A. I., Ning, D., You, Y., & Chen, J. (2026) · Buildings · doi:10.3390/buildings16010196 · Cited by 1 · see citing works
“Third, hallucinated recommendations in HVAC, energy-management, or fault-diagnosis settings can introduce safety risks [7].”
InfluentialWang, X., Shen, H., Qi, H., & Dai, Y. (2026) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3799423 · Cited by 1 · see citing works
Revisiting the role of linguistic knowledge in large language models through prompting
Sataer, Y., Gao, M., Shi, C., Li, B., & Gao, Z. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115667 · Cited by 1 · see citing works
Scaling sensor metadata extraction for exposure health using LLMs
Shah-Mohammadi, F., Im, S., Facelli, J. C., Cummins, M. R., & Gouripeddi, R. (2026) · Exposome · doi:10.1093/exposome/osag008 · Cited by 1 · see citing works
“By leveraging techniques such as named entity recognition, relation extraction, and language modeling, 21-23 NLP systems can automatically identify, structure, and harmonize relevant metadata fields embedded in sensor documentation.”
Vázquez, S. G., Bertolini, L., Ceresa, M., Consoli, S., & Acosta, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21480-5_17 · Cited by 1 · see citing works
Neuberger, J., van der Aa, H., Khrop, I., & López, H. A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-28117-3_9 · Cited by 1 · see citing works
Bougzime, O., Jabbar, S., Cruz, C., & Demoly, F. (2026) · Neurosymbolic Artificial Intelligence · doi:10.1177/29498732261469485 · Cited by 1 · see citing works
Dong, Y., Yang, R., Liu, J., & Qin, X. (2024) · IEEE Access · doi:10.1109/access.2024.3452720 · Cited by 3 · see citing works
“Relation extraction (RE) [1] stands as a crucial task in Natural Language Processing (NLP), dedicated to extracting relations between entities from sentences, which can be leveraged for other advanced tasks [2], [3].”
BackgroundTrajectory Privacy-Protection Mechanism Based on Multidimensional Spatial–Temporal Prediction
Xi, J., Shi, M., Zhang, W., Xu, Z., & Liu, Y. (2024) · Symmetry · doi:10.3390/sym16091248 · Cited by 1 · see citing works
Neuradicon: Operational representation learning of neuroimaging reports
Watkins, H., Gray, R., Julius, A., Mah, Y., Teo, J., Pinaya, W. H. L., Wright, P. K., Jha, A., Engleitner, H., Cardoso, M. J., et al. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108638 · Cited by 3 · see citing works
An Adaptive Hybrid Prototypical Network for Interactive Few-Shot Relation Extraction
Liu, B., Liu, S., Huang, S., & Zheng, L. (2025) · Electronics · doi:10.3390/electronics14071344 · Cited by 1 · see citing works
Yu, F., Zhang, P., Qiao, S., Ding, X., Lu, T., & Gu, N. (2025) · ACM Transactions on the Web · doi:10.1145/3723349 · Cited by 1 · see citing works
Li, L. (2025) · Journal of Computational Methods in Sciences and Engineering · doi:10.1177/14727978251348625
Defining Classes and Semantic Relationships from User Scenarios Through a Heuristic Approach
Aunsa, A. D. H., Barletta, V. S., Buono, P., Faisal, C. M. N., Piccinno, A., Saeed, Z., & Provenzano, L. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-95452-8_21
Using ER Algorithm to Improve Information Retrieval Efficiency in Big Data Environment
Ma, Q. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-8003-0_34
γ -CRD:Gamma-Cooperative Retrieval Diffusion Model for Robust Incomplete Multimodal Learning
Dai, R., Zhu, W., Wang, Z., Meng, H., Yuan, Z., Yan, Y., & Mo, L. (2025) · International Conference on Multimedia Retrieval · doi:10.1145/3731715.3733498
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Enhancing Person-Centric Relation Extraction through Multi-Task LearningPreprint
Wang, H., Niu, W., Ren, H., Li, J., Tian, J. X., & Zhang, D. (2025) · Research Square · doi:10.21203/rs.3.rs-6904071/v1
Li, H., Gao, K., Tian, Z., & Chen, Y. (2025) · International Symposium on Computer Technology and Information Science (ISCTIS) · doi:10.1109/isctis65944.2025.11065717
“Relation Extraction (RE) [1], as one of the key tasks in natural language processing, aims to identify semantic relationships between entities in unstructured text.”
Retrieval-Augmented Generation with Mixed Sentence-BERT for Relation Extraction
Luo, J., Fang, W., Guo, J., & Qin, L. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9994-0_24
Reading papers: Extraction of molecular interaction networks with large language modelsPreprint
Gjerga, E., Wiesenbach, P., & Dieterich, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.21.665999
A Step towards Interpretable Multimodal AI Models with MultiFIX
Malafaia, M., Schlender, T., Alderliesten, T., & Bosman, P. A. N. (2025) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3712255.3734292
Reduction of supervision for biomedical knowledge discovery
Theodoropoulos, C., Coman, A. C., Henderson, J., & Moens, M. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06187-0
Entity-Dependency Memory-Enhanced Document-Level Relation Extraction
He, L., Li, H., Duan, J., Liu, J., Wang, H., & Zhang, Q. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04630-7_12
Prompting LLMs for Relation Classification in Portuguese: Is It Worth It?
Pinto, T., Ferreira, B., Silva, C., & Oliveira, H. G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05179-0_19
- Authors
- Zhao, X., Deng, Y., Yang, M., Wang, L., Zhang, R., Cheng, H., Lam, W., Shen, Y., & Xu, R.
- Affiliations
- Chinese University of Hong Kong (HK)
- Singapore Management University (SG)
- Chinese Academy of Sciences (CN)
- Huazhong University of Science and Technology (CN)
- Sun Yat-sen University (CN)
- Harbin Institute of Technology (CN)
- ORCID
- Venue
- ACM Computing Surveys
- Published
- 2024-07-22
- Volume
- 56
- Issue
- 11
- Pages
- 1-39
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W4399978597
- Cited by
- 168
- References
- 154
- Citations by year
- 2026: 66 · 2025: 89 · 2024: 13
- FWCI
- 24.74
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Computing Surveys (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Research Grant Council of the Hong Kong Special Administrative Region, China (CUHK 14217622)
- National Key Research and Development Program of China (2022YFF0902100)
- National Natural Science Foundation of China (62376262)
- Natural Science Foundation of Guangdong Province of China (2024A1515030166)
- Shenzhen Science and Technology Innovation Program (KQTD20190929172835662)
- Shenzhen Basic Research Foundation (JCYJ20210324115614039)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- relation extraction, entity relationships, pre-trained language models, knowledge graph completion, question answering, evaluation metrics, text representation, context encoding
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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