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Journal of Library and Information Science in Agriculture ›› 2021, Vol. 33 ›› Issue (1): 17-31.doi: 10.13998/j.cnki.issn1002-1248.20-0797

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• Special manuscript • Previous Articles     Next Articles

Building an Artificial Intelligence Engine Based on Scientific and Technological Literature Knowledge

ZHANG Zhixiong1,2,3,4, LIU Huan1,2,4, YU Gaihong1   

  1. 1. National Science Library, Chinese Academy of Sciences, Beijing 100190;
    2. University of Chinese academy of sciences, Beijing 100049;
    3. Wuhan Library, Chinese Academy of Sciences, Wuhan 430071;
    4. Hubei Key Laboratory of Big Data in Science and Technology, Wuhan 430071
  • Received:2020-09-29 Online:2021-01-05 Published:2021-02-05

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Abstract: [Purpose/Significance] How to use the knowledge in the scientific and technological literature to train and improve the model of deep learning algorithm, and acquire knowledge and discover knowledge is an important subject of information research. In order to fully mine and utilize the value of literature knowledge, this paper proposes the goal of building an artificial intelligence (AI) engine based on scientific and technological literature knowledge. [Method/Process] It chooses the literature and information science work as the starting point and takes the scientific and technological literature as the most important carrier of human knowledge. This paper explores the essence of the rapid breakthrough of AI, and innovatively puts forward the construction idea of "science and technology knowledge engine" which is the transformation from "science and technology literature library" in the field of information science. [Results/Conclusions] This paper discusses the construction practice of AI engine based on scientific and technological literature knowledge and explores the method of using the deep learning technology to excavate knowledge to serve information research, so as to provide reference for peers.

Key words: scientific and technological literature, artificial intelligence (AI) knowledge engine, pre-trained language model, fine-tunning model, construction practice of AI engine

CLC Number: 

  • G250
[1] BELTAGY I, KYLE L, ARMAN C.SciBERT: A pretrained language model for scientific text[J]. arXiv preprint arXiv:1903.10676, 2019.
[2] WAN H, ZHANG Y, ZHANG J, et al.AMiner: Search and mining of academic social networks[J]. Data intelligence, 2019, 1(1): 58-76.
[3] SHOHAM Y, PERRAULT R, BRYNJOLFSSON E, et al.Artificial intelligence index 2017 annual report[J]. Artificial intelligence index, 2017.
[4] WU Y, SCHUSTER M, CHEN Z, et al. Google's neural machine translation system: Bridging the gap between human and machine translation[J]. ArXiv preprint arxiv:1609.08144, 2016.
[5] MCCULLOCH W S, PITTS W.A logical calculus of the ideas immanent in nervous activity[J]. The bulletin of mathematical biophysics, 1943, 5(4): 115-133.
[6] HOCHREITER S, SCHMIDHUBER J.Long short-term memory[J]. Neural computation, 1997, 9(8): 1735-1780.
[7] LECUN Y, BOSER B, DENKER J S, et al.Backpropagation applied to handwritten zip code recognition[J]. Neural computation, 1989, 1(4): 541-551.
[8] GOODFELLOW I, BENGIO Y, COURVILLE A.Deep learning[M]. Cambridge: MIT press, 2016.
[9] 雷明. 机器学习与应用[M]. 北京: 清华大学出版社, 2019.
LEI M.Machine learning and application[M]. Beijing: Tsinghua university press, 2019.
[10] DEVLIN J, CHANG M W, LEE K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding[J]. ArXiv preprint arxiv:1810.04805, 2018.
[11] PETERS M E, NEUMANN M, IYYER M, et al. Deep contextualized word representations[J]. ArXiv preprint arxiv:1802.05365, 2018.
[12] RADFORD A, NARASIMHAN K, SALIMANS T, et al.Improving language understanding by generative pre-training[J]. URL https://s3-us-west-2.amazonaws.com/openai-assets/researchcovers/languageunsupervised/language understanding paper.pdf, 2018.
[13] RADFORD A, WU J, CHILD R, et al.Language models are unsupervised multitask learners[J]. OpenAI blog, 2019, 1(8).
[14] YANG Z, DAI Z, YANG Y, et al.XLNet: Generalized autoregressive pretraining for language understanding[J]. ArXiv preprint arxiv:1906.08237, 2019.
[15] LIU Y, OTT M, GOYAL N, et al.Roberta: A robustly optimized Bert pretraining approach[J]. ArXiv preprint arxiv:1907.11692, 2019.
[16] WANG A, SINGH A, MICHAEL J, et al. Glue: A multi-task benchmark and analysis platform for natural language understanding[J]. ArXiv preprint arxiv:1804.07461, 2018.
[17] SUN Y, WANG S, LI Y, et al.ERNIE: Enhanced representation through knowledge integration[J]. ArXiv preprint arxiv:1904.09223,2019.
[18] SUN Y, WANG S, LI Y, et al.ERNIE 2.0: a continual pre-training framework for language understanding[J]. ArXiv preprint arxiv:1907.12412, 2019.
[19] CUI Y, CHE W, LIU T, et al.Pre-Training with whole word masking for Chinese BERT[J]. ArXiv preprint arxiv:1906.08101, 2019.
[20] OpenCLaP. 多领域开源中文预训练语言模型仓库项目简介[EB/OL]. [2020-08-07]. http://zoo.thunlp.org/.
OpenCLaP. Open Chinese language pre-trained model zoo project brief[EB/OL]. [2020-08-07]. http://zoo.thunlp.org/.
[21] 谢玮, 沈一, 马永征. 基于图计算的论文审稿自动推荐系统[J]. 计算机应用研究, 2016, 33(3): 798-801.
XIE W, SHEN Y, MA Y Z.Recommendation system for paper reviewing based on graph computing[J]. Application research of computers, 2016, 33(3): 798-801.
[22] WANG D, LIANG Y, XU D, et al.A Content-Based recommender system for computer science publications[J]. Knowledge-Based systems, 2018: S0950705118302107.
[23] 于改红, 张智雄, 马娜. 科技文献语篇元素自动标注模型研究综述[J]. 图书情报工作, 2018, 62(15): 132-144.
YU G H, ZHANG Z X, MA N.Overview of science and technology literature discourse elements automatic annotation model research[J]. Library and information service, 2018, 62(15): 132-144.
[24] HéLèNE D R, FALQUET G. An automated annotation process for the SciDocAnnot scientific document model[C]//5th international workshop on semantic digital archives, 2015.
[25] LIAKATA M, TEUFEL S, SIDDHARTHAN A, et al.Corpora for the conceptualisation and zoning of scientific papers[C]//Proceedings of the international conference on language resources and evaluation, LREC 2010, 17-23 may 2010, Valletta, Malta: DBLP, 2010.
[26] TEUFEL S, SIDDHARTHAN A, BATCHELOR C.Towards discipline-independent argumentative zoning[C]//The 2009 conference. association for computational linguistics, 2009.
[27] FISAS B, RONZANO F, SAGGION H.A multi-layered annotated corpus of scientific papers[C]//LREC 2016, 2016.
[28] 张智雄, 刘欢, 丁良萍, 等. 不同深度学习模型的科技论文摘要语步识别效果对比研究[J]. 数据分析与知识发现, 2019, 3(12):1-9.
ZHANG Z X, LIU H, DING L P, et al.Identifying moves of research abstracts with deep learning methods[J]. Data analysis and knowledge discovery, 2019, 3(12): 1-9.
[29] YU G, ZHANG Z, LIU H, et al.Masked sentence model based on BERT for move recognition in medical scientific abstracts[J], 2019.
[30] DERNONCOURT F, LEE J Y. Pubmed 200k rct: a dataset for sequential sentence classification in medical abstracts[J]. ArXiv preprint arxiv:1710.06071, 2017.
[31] JIN D, PETER S. Hierarchical neural networks for sequential sentence classification in medical scientific abstracts[J]. arXiv preprint arXiv:1808.06161, 2018.
[32] 赵旸, 张智雄, 刘欢, 等. 基于BERT模型的中文医学文献分类研究[J/OL]. 数据分析与知识发现: 1-12[2020-08-05]. http://kns.cnki.net/kcms/detail/10.1478.G2.20200603.1457.004.html.
ZHAO Y, ZHANG Z X, LIU H, et al. Classification of Chinese medical literature with BERT model[J/OL]. Data analysis and knowledge discovery: 1-12[2020-08-05]. http://kns.cnki.net/kcms/detail/10.1478.G2.20200603.1457.004.html.
[33] DING L P, ZHANG Z X, LIU H, et al.Automatic keyphrase extraction from scientific Chinese medical abstracts based on character-level sequence labeling. In Wuhan’20: Joint conference on digital library, Wuhan, 2020.
[34] GERARD S, YANG C S, CLEMENT T Y.A theory of term importance in automatic text analysis[J]. Journal of the American society for Info rmation Science, 1975, 26(1): 33-44.
[35] Huang Z H, XU W, YU K. Bidirectional LSTM-CRF models for sequence tagging[J]. arXiv preprint arXiv:1508.01991, 2015.
[36] NAVIGLI, ROBERTO, PAOLA V.Learning word-class lattices for definition and hypernym extraction[C]. Proceedings of the 48th annual meeting of the association for computational linguistics, 2010.
[37] DONG L, YANG N, WANG W, et al.Unified language model pre-training for natural language understanding and generation[C]//Advances in neural information processing systems, 2019: 13063-13075.
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