| [1] |
Mohamed SK, Nová?ek V, Nounu A. Discovering protein drug targets using knowledge graph embeddings[J]. Bioinformatics, 2020, 36(2): 603-610.
|
| [2] |
Lukashina N, Kartysheva E, Spjuth O, et al. SimVec: predicting polypharmacy side effects for new drugs[J]. J Cheminform, 2022, 14(1): 49.
|
| [3] |
Li ZX. Relocation of Parkinson''s disease drugs based on knowledge graph[J]. Inf Technol (信息技术与信息化), 2022(7): 28-32.
|
| [4] |
Wu XD, Sheng SJ, Jiang TT, et al. Huapu-CP:From knowledge graphs to a data central-platform[J]. JAS (自动化学报), 2020(10): 2045-2059.
|
| [5] |
Fan YY, Li ZM. Research and application progress of Chinese medical knowledge graph[J]. J Front Comput Sci Technol (计算机科学与探索), 2022, 16(10): 2219-2233.
|
| [6] |
Qi GL, Gao H, Wu TX. Research progress of knowledge map[J]. Inf Eng(情报工程), 2017, 3(1): 4-25.
|
| [7] |
Ma XG. Knowledge graph construction and application in geosciences: a review[J]. Comput Geosci, 2022, 161: 105082.
|
| [8] |
Li ZW, Ding Y, Hua ZY, et al. Knowledge graph completion model based on triplet importance integration[J]. Comput Sci (计算机科学), 2020, 47(11): 231-236.
|
| [9] |
Hu JH, Zhao WQ, Fang A. Research on clinical text processing and knowledge discovery method based on medical big data[J]. China Digit Med (中国数字医学), 2020, 15(7): 11-13, 88.
|
| [10] |
Guo XY, He TT. A survey of information extraction[J]. Comput Sci (计算机科学), 2015, 42(2): 14-17,38.
|
| [11] |
de Aquino Silva R, da Silva L, Dutra ML, et al. An improved NER methodology to the Portuguese language[J]. Mobile Netw Appl, 2021, 26(1): 319-325.
|
| [12] |
Liu P, Guo YM, Wang FL, et al. Chinese named entity recognition: the state of the art[J]. Neurocomputing, 2022, 473: 37-53.
|
| [13] |
Wu ST, Liu HF, Li DC, et al. Unified Medical Language System term occurrences in clinical notes: a large-scale corpus analysis[J]. J Am Med Inform Assoc, 2012, 19(e1): e149-e156.
|
| [14] |
Friedman C, Alderson PO, Austin JH, et al. A general natural-language text processor for clinical radiology[J]. J Am Med Inform Assoc, 1994, 1(2): 161-174.
|
| [15] |
Chiticariu L, Krishnamurthy R, Li YY, et al. Domain adaptation of rule-based annotators for named-entity recognition tasks[C]//Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. Cambridge, Massachusetts. New York: ACM, 2010: 1002–1012.
|
| [16] |
Eddy SR. Hidden Markov models[J]. Curr Opin Struct Biol, 1996, 6(3): 361-365.
|
| [17] |
Lafferty J, McCallum A, Pereira F. Conditional random fields: probabilistic models for segmenting and labeling sequence dat[C]. ICML. New York:Association for Computing Machinery, 2001:282-289.
|
| [18] |
Cortes C, Vapnik V. Support-vector networks[J]. Mach Learn, 1995, 20: 273-297.
|
| [19] |
Zhang CS, Guo JY, Xian YT, et al. English product named entity recognition based on conditional random field[J]. Comput Sci Eng (计算机工程与科学), 2010, 32 (6): 115-117.
|
| [20] |
Elman JL. Finding structure in time[J]. Cogn Sci, 1990, 14(2): 179-211.
|
| [21] |
Cai LQ, Zhou ST, Yan X, et al. A stacked BiLSTM neural network based on coattention mechanism for question answering[J]. Comput Intell Neurosci, 2019, 2019: 9543490.
|
| [22] |
Xu YS, Li L, Gao HH, et al. Sentiment classification with adversarial learning and attention mechanism[J]. Comput Intell, 2021, 37(2): 774-798.
|
| [23] |
Vaswani A, Shazeer N, Parmar N, et al. Attention is all You need[J]. arXiv,2017:1706.03762.
|
| [24] |
Devlin J, Chang MW, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[J]. arXiv,2018: 1810.04805
|
| [25] |
Song YH, Tian SW, Yu L. A method for identifying local drug names in Xinjiang based on BERT-BiLSTM-CRF[J]. Autom Control Comput Sci, 2020, 54(3): 179–190.
|
| [26] |
Chen LM, Liu D, Yang JK, et al. Construction and application of COVID-19 infectors activity information knowledge graph[J]. Comput Biol Med, 2022, 148: 105908.
|
| [27] |
Xu L, Li JH. Biomedical named entity recognition based on BERT and BiLSTM-CRF[J]. Comput Sci Eng, 2021(10): 1873-1879.
|
| [28] |
Hou YT, Abduklimu A, Haridamu A. Research progress of Chinese pre training model[J]. Comput Sci (计算机科学), 2022, 49(7): 148-163.
|
| [29] |
Cui YM, Che WX, Liu T, et al. Pre-training with whole word masking for Chinese BERT[J]. IEEE/ACM Trans Audio Speech Lang Process, 2021, 29: 3504-3514.
|
| [30] |
Song SL, Zhang N, Huang HT. Named entity recognition based on conditional random fields[J].Clust Comput, 2019, 22(3): 5195-5206.
|
| [31] |
Wei ZP, Su JL, Wang Y, et al. A novel cascade binary tagging framework for relational triple extraction[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020: 1476-1488.
|
| [32] |
Zheng SC, Wang F, Bao HY, et al. Joint extraction of entities and relations based on a novel tagging scheme[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Vancouver, Canada. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017: 1227-1236.
|
| [33] |
Luque A, Carrasco A, Martín A, et al. The impact of class imbalance in classification performance metrics based on the binary confusion matrix[J]. Pattern Recognit, 2019, 91: 216-231.
|
| [34] |
Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks[J]. Inf Process Manag, 2009, 45(4): 427-437.
|
| [35] |
Sen S, Mehta A, Ganguli R, et al. Recommendation of influenced products using association rule mining: Neo4j as a case study[J]. SN Comput Sci, 2021, 2(2): 1-17.
|
百度学术
下载:
