南美白对虾养殖领域中文命名实体识别数据集构建

doi:10.3778/j.issn.1002-8331.2312-0157

摘要/Abstract

摘要： 该研究致力于构建一个高质量的数据集，用于南美白对虾养殖领域的命名实体识别（named entity recognition，NER）任务，命名为VamNER。为确保数据集的多样性，从CNKI数据库中收集了近10年的高质量论文，并结合权威书籍进行语料构建。邀请专家讨论实体类型，并经过专业培训的标注人员使用IOB2标注格式进行标注，标注过程分为预标注和正式标注两个阶段以提高效率。在预标注阶段，标注者间一致性（inter-annotation agreement，IAA）达到0.87，表明标注人员的一致性较高。最终，VamNER包含6?115个句子，总字符数达384?602，涵盖10个实体类型，共有12?814个实体。研究通过与多个通用领域数据集和一个特定领域数据集进行比较，揭示了VamNER的独特特性。在实验中使用了预训练的基于变换器的双向编码器表示（bidirectional encoder representations from Transformers，BERT）模型、双向长短期记忆神经网络（bidirectional long short-term memory network，BiLSTM）和条件随机场模型（conditional random fields，CRF），最优模型在测试集上的F1值达到82.8%。VamNER成为首个专注于南美白对虾养殖领域的NER数据集，为中文特定领域NER研究提供了丰富资源，有望推动水产养殖领域NER研究的发展。

关键词: 命名实体识别, VamNER数据集, 标注者间一致性（IAA）, 基于变换器的双向编码器表示（BERT）, 双向长短期记忆神经网络（BiLSTM）, 条件随机场（CRF）

Abstract: This research is dedicated to constructing a high-quality dataset for the named entity recognition (NER) task in the field of penaeus vannamei farming, named VamNER. In order to ensure the diversity of the dataset, high-quality papers in the past 10 years have collected from the CNKI database and combined with authoritative books for corpus construction. Experts are invited to discuss entity types, and professionally trained annotators use the IOB2 annotation format to annotate. The annotation process is divided into two stages: pre-annotation and formal annotation to improve efficiency. In the pre-annotation stage, the consistency between annotators reached 0.87, indicating that the consistency of annotators is high. Finally, VamNER contains 6?115 sentences with a total number of characters of 384?602, covering 10 entity types and a total of 12?814 entities. The study reveals the unique properties of VamNER through comparing with multiple domain-general datasets and one domain-specific dataset. In the experiment, the pre-trained bidirectional encoder representations from Transformers (BERT) model, bidirectional long short-term memory network (BiLSTM) and conditional random fields(CRF) model are used. The F1 value of the optimal model on the test set reaches 82.8%. VamNER has become the first NER dataset focusing on the field of penaeus vannamei farming, providing rich resources for NER research in specific Chinese fields, and is expected to promote the development of NER research in the aquaculture field.

Key words: named entity recognition, VamNER dataset, inter-annotation agreement (IAA), bidirectional encoder representations from Transformers (BERT), bidirectional long short-term memory network (BiLSTM)； conditional random fields (CRF)

彭小红, 邓峰, 余应淮. 南美白对虾养殖领域中文命名实体识别数据集构建[J]. 计算机工程与应用, 2025, 61(9): 353-362.

PENG Xiaohong, DENG Feng, YU Yinghuai. Construction of Chinese Named Entity Recognition Dataset in Penaeus Vannamei Farming Field[J]. Computer Engineering and Applications, 2025, 61(9): 353-362.

参考文献

[1] NADEAU D, SEKINE S. A survey of named entity recognition and classification[J]. Lingvisticae Investigationes, 2007, 30(1): 3-26.
[2] HAO X J, JI Z, LI X H, et al. Construction and application of a knowledge graph[J]. Remote Sensing, 2021, 13(13): 2511.
[3] BOUZIANE A, BOUCHIHA D, DOUMI N, et al. Question answering systems: survey and trends[J]. Procedia Computer Science, 2015, 73: 366-375.
[4] STAHLBERG F. Neural machine translation: a review[J]. Journal of Artificial Intelligence Research, 2020, 69: 343-418.
[5] ZHANG Y, MENG F D, CHEN Y F, et al. Target-oriented fine-tuning for zero-resource named entity recognition[C]//Proceedings of the Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Stroudsburg: ACL, 2021.
[6] 张海耿, 吴小茜, 姜世豪, 等. 不同养殖模式对南美白对虾生长性能及肠道菌群的影响[J]. 渔业现代化, 2023, 50(3): 27-35.
ZHANG H G, WU X Q, JIANG S H, et al. Growth performance and intestinal bacterial community structure of Litopenaeus vannamei under different cultivation systems[J]. Fishery Modernization, 2023, 50(3): 27-35.
[7] LIU Z H, XU Y, YU T Z, et al. CrossNER: evaluating cross-domain named entity recognition[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2021: 13452-13460.
[8] POIBEAU T, KOSSEIM L. Proper name extraction from non-journalistic texts[C]//Proceedings of the Eleventh Computational Linguistics in the Netherlands Meeting, Tilburg, November 3, 2000: 144-157.
[9] LIU P, GUO Y M, WANG F L, et al. Chinese named entity recognition: the state of the art[J]. Neurocomputing, 2022, 473: 37-53.
[10] XU J J, HE H F, SUN X, et al. Cross-domain and semisupervised named entity recognition in Chinese social media: a unified model[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018, 26(11): 2142-2152.
[11] ZHANG N Y, CHEN M S, BI Z, et al. CBLUE: a Chinese biomedical language understanding evaluation benchmark[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2022.
[12] 杨崇洛, 生龙, 魏忠诚, 等. 新冠文本实体关系抽取及数据集构建方法研究[J]. 计算机工程与应用, 2023, 59(8): 97-104.
YANG C L, SHENG L, WEI Z C, et al. Research on COVID-19 text entity relation extraction and dataset construction methods[J]. Computer Engineering and Applications, 2023, 59(8): 97-104.
[13] 马凯, 田苗, 谭永健, 等. 基于四份区域地质调查报告构建的命名实体识别试验数据集研发[J]. 全球变化数据学报(中英文), 2022, 6(1): 78-84.
MA K, TIAN M, TAN Y J, et al. Development of a named entity recognition dataset based on four regional geological survey reports[J]. Journal of Global Change Data & Discovery, 2022, 6(1): 78-84.
[14] 谢志强, 刘金柱, 刘根辉. 古汉语嵌套命名实体识别数据集的构建和应用研究[C]//第二十一届中国计算语言学大会, 2022.
XIE Z Q, LIU J Z, LIU G H. Construction and application of classical Chinese nested named entity recognition data set[C]//Proceedings of the 21st Chinese National Conference on Computational Linguistics, 2022.
[15] BRANDSEN A, VERBERNE S, WANSLEEBEN M, et al. Creating a dataset for named entity recognition in the archaeology domain [C]//Proceedings of the Twelfth Language Resources and Evaluation Conference, 2020.
[16] LEITNER E, REHM G, MORENO-SCHNEIDER J. A dataset of German legal documents for named entity recognition[J]. arXiv:2003.13016, 2020.
[17] 曹煜成, 文国樑, 李卓佳, 等. 南美白对虾高效养殖与疾病防治技术[M]. 北京: 化学工业出版社, 2014.
CAO Y C, WEN G L, LI Z J. Efficient culture and disease control techniques of Penaeus vannamei[M]. Beijing: Chemical Industry Press, 2014.
[18] 林文辉, 苏跃朋. 池塘里的那些事儿: 养好池塘就是养好了南美白对虾[M]. 北京: 中国农业出版社, 2017.
LIN W H, SU Y P. What happens in a pond[M]. Beijing: China Agriculture Press, 2017.
[19] GOODMAN M W, GEORGI R, XIA F. PDF to-text reanalysis for linguistic data mining[C]//Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), 2018.
[20] 刘志轩, 王印庚, 张正, 等. 几种消毒剂对凡纳滨对虾致病性弧菌的杀灭作用[J]. 渔业科学进展, 2018, 39(3): 112-119.
LIU Z X, WANG Y G, ZHANG Z, et al. Germicidal effect of several disinfectants on the pathogenic bacteria of acute hepatopancreatic necrosis disease(AHPND) in litopenaeus vannamei[J]. Progress in Fishery Sciences, 2018, 39(3): 112-119.
[21] 刘梅, 原居林, 何海生, 等. 微藻在南美白对虾养殖废水中的生长及净化效果[J]. 应用与环境生物学报, 2018, 24(4): 866-872.
LIU M, YUAN J L, HE H S, et al. Removal of nitrogen and phosphorus by eight strains of microalgae and their growth characteristics in Penaeus vannamei sewage[J]. Chinese Journal of Applied and Environmental Biology, 2018, 24(4): 866-872.
[22] 周明月, 龚晨, 李正华, 等. 数据标注方法比较研究: 以依存句法树标注为例[J]. 清华大学学报(自然科学版), 2022, 62(5): 908-916.
ZHOU M Y, GONG C, LI Z H, et al. Comparison of data annotation approaches using dependency tree annotation as a case study[J]. Journal of Tsinghua University (Science and Technology), 2022, 62(5): 908-916.
[23] COHEN J. A coefficient of agreement for nominal scales[J]. Educational and Psychological Measurement, 1960, 20(1): 37-46.
[24] GWET K L. Computing inter-rater reliability and its variance in the presence of high agreement[J]. British Journal of Mathematical and Statistical Psychology, 2008, 61(1): 29-48.
[25] PAN J, ZHANG C H, WANG H J, et al. A comparative study of Chinese named entity recognition with different segment representations[J]. Applied Intelligence, 2022, 52(11): 12457-12469.
[26] LIU Y J, CHE W X, QIN B, et al. Exploring segment representations for neural semi-Markov conditional random fields[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2020, 28: 813-824.
[27] RAMSHAW L A, MARCUS M P. Text chunking using transformation-based learning[M]//Natural language processing using very large corpora. Dordrecht: Springer Netherlands, 1999: 157-176.
[28] CHANG D J, CHEN M S, LIU C Z, et al. DiaKG: an annotated diabetes dataset for medical knowledge graph construction[C]//Proceedings of the 6th China Conference on Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction, 2021: 308-314.
[29] DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[J]. arXiv:1810.04805, 2018.
[30] YU Y, SI X S, HU C H, et al. A review of recurrent neural networks: LSTM cells and network architectures[J]. Neural Computation, 2019, 31(7): 1235-1270.
[31] HUANG Z H, XU W, YU K. Bidirectional LSTM-CRF models for sequence tagging[J].arXiv:1508.01991, 2015.
[32] 鄂海红, 张文静, 肖思琪, 等. 深度学习实体关系抽取研究综述[J]. 软件学报, 2019, 30(6): 1793-1818.
E H H, ZHANG W J, XIAO S Q, et al. Survey of entity relationship extraction based on deep learning[J]. Journal of Software, 2019, 30(6): 1793-1818.
[33] LIU Z W, MIAO Z Q, ZHAN X H, et al. Large-scale long-tailed recognition in an open world[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 2532-2541.