Computer Science ›› 2023, Vol. 50 ›› Issue (7): 229-236.doi: 10.11896/jsjkx.220500068
• Artificial Intelligence • Previous Articles Next Articles
KONG Jiabin, LYU Jianwen, LIU Jiangnan, DU Wenxuan
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[1]HE M,GONG C C,ZHANG H P,et al.Method of New WordIdentification Based on Lager- scale Corpus[J].Computer Engineering and Applications,2007,43(21):157-159. [2]ZHAO H,CAI D,HUANG C N,et al.Chinese Word Segmentation:Another Decade Review(2007-2017) [DB/OL].https://arxiv.org/ftp/arxiv/papers/1901/1901.06079.pdf. [3]LIU L,WANG D B.A Review on Named Entity Recognition[J].Journal of the China Society for Scientific and Technical Information,2018,37(3):329-340. [4]SUN Z,WANG H L.Overview on the Advance of the Research on Named Entity Recognition[J].Data Analysis and Knowledge Discovery,2010,193(6):42-47. [5]CHEN Q Y,CHENG G,LI D,et al.Named Entity Recognition for Mechanical Design and Manufacturing Area[J].Computer Engineering and Applications,2017,53(20):100-104. [6]VIKAS Y,STEVEN B.A Survey on Recent Advances in NamedEntity Recognition from Deep Learning models [C]//Procee-dings of the 27th International Conference on Computational Linguistics.2018:2145-2158. [7]PAN Z G.Research on the Recognition of Chinese Named EntityBased on Rulesand Statistics[J].Information Science,2012,30(5):708-712,786. [8]MAO X L,LI F F,WANG H T,et al.Named Entity Recognition of Electronic Medical Record Based on Improved HMM Algorithm[C]//2017 International Conference on Computer Technology,Electronics and Communication(ICCTEC).IEEE,2017:435-438. [9]JU Z F,WANG J,ZHU F.Named Entity Recognition from Biomedical Text Using SVM[C]//2011 5th International Confe-rence on Bioinformatics and Biomedical Engineering.IEEE,2011:1-4. [10]SUN A,YU Y X,LUO Y G,et al.Research on Feature Extraction Scheme of Chinese-character Granularity in Sequence Labeling Model--A Case Study About Clinical Named Entity Recognition of CCKS2017:Task2[J].Library and Information Ser-vice,2018,62(11):103-111. [11]DONG C H,WU H J,ZHANG J J,et al.Multichannel LSTM-CRF for Named Entity Recognition in Chinese Social Media[C]//China National Conference on Chinese Computational Linguistics International Symposium on Natural Language Proces-sing Based on Naturally Annotated Big Data.2017:197-208. [12]LI Y,MA L,SHAO D G,et al.Chinese Named Entity Recognition for Social Media[J].Journal of Chinese Information Processing,2020,34(8):61-69. [13]LI M Y,KONG F.Combined Self-Attention Mechanism forNamed Entity Recognition in Social Media[J].Journal of Tsinghua University(Science and Technology),2019,59(6):461-467. [14]BATISTA-NAVARRO R,RAK R,ANANIADOU S.Optimizing Chemical Named Entity Recognition with Pre-processing Analytics,Knowledge-Rich Features and Heuristics[J].Journal of Cheminformatics,2015,7(Suppl 1):S6. [15]YANG P,YANG Z H,LUO,et al.An Attention-Based Ap-proach for Chemical Compound and Drug Named Entity Recognition[J].Journal of Computer Research and Development,2018,55(7):1548-1556. [16]LI X,WEI X H,JIA L,et al.Recognition of Crops,Diseases and Pesticides Named Entities in Chinese Based on Conditional Random Fields[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(S1):178-185. [17]FENG Y T,ZHANG H J,HAO W N.Named Entity Recognition for Military Text[J].Computer Science,2015,42(7):15-18,47. [18]SHAN Y D,WANG H J,WANG N.Military Domain Named Entity Recognition Based on Multi-label[J].Computer Science,2019,46(S2):9-12. [19]WANG Z X,QIU Q Y,FENG P E,et al.Information Extraction Method of Technical Solution from Mechanical Product Patent[J].Journal of Mechanical Engineering,2009,45(10):198-206. [20]FANTONI G,APREDA R,DELL’ORLETTA F,et al.Automatic Extraction of Function-Behaviour-State Information from Patents[J].Advanced Engineering Informatics,2013,27(3):317-334. [21]ALEX J,HINRICH S,SOREN B.Unsupervised Training SetGeneration for Automatic Acquisition of Technical Terminology in Patents [C]//Proceedings of COLING 2014,the 25th International Conference on Computational Linguistics:Dublin,Ireland,2014,Technical Papers.2014:290-300. [22]CHEN L,XU S,ZHU L,et al.A deep Learning Based Method for Extracting Semantic Information from Patent Documents[J].Scientometrics,2020,125:289-312. [23]LI S B,WU Y M,XU Y X,et al.A Bayesian Network BasedAdaptability Design of Product Structures for Function Evolution [J].Applied Sciences,2018,8(4):493-509. [24]WANG M P,WANG H,DENG S H,et al.Extracting Chinese Metallurgy Patent Terms with Conditional Random Fields[J].Data Analysis and Knowledge Discovery,2016,271(6):28-36. [25]YU Y,ZHAO N X.Patent Term Extraction Based on GenericWords and Term Components[J].Journal of the China Society for Scientific and Technical Information,2018,37(7):742-752. [26]CHEN M J,XIE Z P,CHEN X Q,et al.Novel Bidirectional Aggregation Degree Feature Extraction Method for Patent New Word Discovery[J].Journal of Computer Applications,2020,40(3):631-637. [27]LI J,JING F Y,LIU J.Study on Patent Entity Extraction Based on Improved Bert Algorithms-A Case Study of Graphene[J].Journal of University of Electronic Science and Technology of China,2020,49(6):883-890. [28]GEORGESCU T M,IANCU B,ZAMFIROIU A,et al.A Survey on Named Entity Recognition Solutions Applied for Cybersecurity-Related Text Processing[C]//Proceedings of Fifth International Congress on Information and Communication Technology,ICICT 2020,London,(Volume 2).2020:316-325. |
[1] | DENG Liang, CAO Cun-gen. Methods of Patent Knowledge Graph Construction [J]. Computer Science, 2022, 49(11): 185-196. |
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