Semantic Matching Model for Chinese Scientific Datasets

NAVIGATE

Table of Contents

STATISTICS

Viewed420

Downloads377

Semantic Matching Model for Chinese Scientific Datasets

[HTML] PDF下载 (377)

[1]LIU Jianping,CHU Xintao,WANG Jian,et al.Semantic Matching Model for Chinese Scientific Datasets[J].Journal of Zhengzhou University (Engineering Science),2024,45(06):56-64.[doi:10.13705/j.issn.1671-6833.2024.03.008]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 06 Page number: 56-64 Column: Public date: 2024-09-25

Title:: Semantic Matching Model for Chinese Scientific Datasets

Author(s):: LIU Jianping¹; 2; CHU Xintao¹; WANG Jian³; GU Xunxun¹; WANG Meng¹; WANG Yingfei¹; 1. College of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China; 2. The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China; 3. Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Keywords:: text matching; semantic matching; pre-training model; scientific datasets; natural language processing

CLC:: TP3-05TP391. 1

DOI:: 10.13705/j.issn.1671-6833.2024.03.008

Abstract:: In order to address the difficulty of existing word-level semantic matching models in understanding sentence-level scientific dataset metadata, a sentence-level semantic matching ( CSDSM) model for Chinese scientificdatasets was proposed. The model used the CSL dataset to train and generate the CoSENT pre-training model basedon SimCSE and CoSENT. Building upon the CoSENT model, a multi-head self-attention mechanism was introducedfor feature extraction, and the final output was obtained by weighting the cosine similarity and KNN classificationresults. Experimental data from the National Earth System Science Data Center′s open semantic metadata information was used as a self-built scientific dataset. The experimental results showed that compared to the Chinese BERTmodel, the proposed model improved the Spearman′s ρ index by 0. 044 8, 0. 029 0, 0. 177 7 and 0. 050 9 on thepublic datasets AFQMC, LCQMC, Chinese-STS-B, and PAWS-X, respectively. Additionally, F1 and Acc on theself-built scientific dataset were improved by 0. 078 8 and 0. 063 4 respectively. The proposed model effectively addresses the problem of sentence-level semantic matching in scientific datasets.

References:: [1] 罗鹏程, 王继民, 王世奇, 等. 基于深度学习的科学数据集检索方法研究[ J] . 情报理论与实践, 2022, 45(7) : 49-56.
LUO P C, WANG J M, WANG S Q, et al. Research ondeep learning based scientific dataset retrieval method[ J] . Information Studies: Theory & Application, 2022,45(7) : 49-56.
[2] CHEN S H, XU T J. Long text QA matching model basedon BiGRU-DAttention-DSSM[ J] . Mathematics, 2021, 9(10) : 1129.
[3] 冯皓楠, 何智勇, 马良荔. 基于图文注意力融合的主题标签推荐[ J] . 郑州大学学报( 工学版) , 2022, 43(6) : 30-35.
FENG H N, HE Z Y, MA L L. Multimodal hashtag recommendation based on image and text attention fusion[ J] . Journal of Zhengzhou University ( Engineering Science) , 2022, 43(6) : 30-35.
[4] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]∥Proceedings of the 31st International Conference on Neural Information Processing Systems. New York: ACM, 2017: 6000-6010.
[5] LI Y D, ZHANG Y Q, ZHAO Z, et al. CSL: a largescale Chinese scientific literature dataset [ EB / OL ] .(2022- 09 - 12 ) [ 2023 - 06 - 11 ] . https:∥arxiv. org /abs/ 2209. 05034.
[6] GAO T Y, YAO X C, CHEN D Q. SimCSE: simple contrastive learning of sentence embeddings [ C] ∥Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association forComputational Linguistics, 2021: 6894-6910.
[7] LI S H, GONG B. Word embedding and text classification based on deep learning methods[ J] . MATEC Web ofConferences, 2021, 336: 06022.
[8] LIU J P, CHU X T, WANG Y F, et al. Deep text retrieval models based on DNN, CNN, RNN and Transformer: a review[ C]∥2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems(CCIS) . Piscataway: IEEE, 2022: 391-400.
[9] HUANG P S, HE X D, GAO J F, et al. Learning deepstructured semantic models for web search using clickthrough data[C]∥Proceedings of the 22nd ACM international conference on Information & Knowledge Management. New York: ACM, 2013: 2333-2338.
[10] SHEN Y L, HE X D, GAO J F, et al. A latent semanticmodel with convolutional-pooling structure for informationretrieval[C]∥Proceedings of the 23rd ACM InternationalConference on Conference on Information and KnowledgeManagement. New York: ACM, 2014: 101-110.
[11] MOHAN S, FIORINI N, KIM S, et al. A fast deeplearning model for textual relevance in biomedical information retrieval [ C ] ∥Proceedings of the 2018 WorldWide Web Conference. New York: ACM, 2018: 77-86.
[12] KHURANA D, KOLI A, KHATTER K, et al. Naturallanguage processing: state of the art, current trends andchallenges[ J] . Multimedia tools and applications, 2023,82(3) : 3713-3744.
[13] MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[C]∥Proceedings of the 26th International Conference on Neural Information Processing Systems:Volume2. New York: ACM, 2013: 3111-3119.
[14] 汪烨, 周思源, 翁知远, 等. 一种面向用户反馈的智能分析与服务设计方法 [ J] . 郑州大学学报 ( 工学版) , 2023, 44(3) : 56-61.
WANG Y, ZHOU S Y, WENG Z Y, et al. An intelligentanalysis and service design method for user feedback[ J] .Journal of Zhengzhou University ( Engineering Science) ,2023, 44(3) : 56-61.
[15] LIU P F, YUAN W Z, FU J L, et al. Pre-train, prompt,and predict: a systematic survey of prompting methods innatural language processing [ J ] . ACM Computing Surveys, 2021,55(9) : 195.
[16] CHOUDHARY S, GUTTIKONDA H, CHOWDHURY DR, et al. Document retrieval using deep learning [ C]∥2020 Systems and Information Engineering Design Symposium ( SIEDS) . Piscataway: IEEE, 2020: 1-6.
[17] ESTEVA A, KALE A, PAULUS R, et al. COVID-19 information retrieval with deep-learning based semanticsearch, question answering, and abstractive summarization[ J] . NPJ Digital Medicine, 2021, 4: 68.
[18] BELTAGY I, LO K, COHAN A. SciBERT: a pretrainedlanguage model for scientific text[C]∥Proceedings of the2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing ( EMNLP-IJC-NLP ) . Stroudsburg: Association for Computational Linguistics, 2019: 3615-3620.
[19] CHOWDHURY A, ROSENTHAL J, WARING J, et al.Applying self-supervised learning to medicine: review ofthe state of the art and medical implementations[ J] . Informatics, 2021, 8(3) : 59.
[20] REIMERS N, GUREVYCH I. Sentence-BERT: sentenceembeddings using siamese BERT-networks[C]∥Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International JointConference on Natural Language Processing ( EMNLPIJCNLP ) . Stroudsburg: Association for ComputationalLinguistics, 2019: 3982-3992.
[21] LI L Y, SONG D M, MA R T, et al. KNN-BERT: finetuning pre-trained models with KNN classifier[ EB / OL] .(2021- 10 - 06 ) [ 2023 - 06 - 11 ] . https:∥arxiv. org /abs/ 2110. 02523.
[22] PALANIVINAYAGAM A, EL-BAYEH C Z, DAMAŠEVICˇIUSR. Twenty years of machine-learning-based text classification: a systematic review [ J ] . Algorithms, 2023, 16(5) : 236.
[23] CHICCO D. Siamese neural networks: an overview[ J] .Methods in Molecular Biology, 2021, 2190: 73-94.
[24] DEVLIN J, CHANG M W, LEE K, et al. BERT: pretraining of deep bidirectional transformers for languageunderstanding[ EB / OL ] . ( 2019 - 05 - 24 ) [ 2023 - 06 -11] . https:∥arxiv. org / abs/ 1810. 04805.
[25] CUI Y M, CHE W X, LIU T, et al. Pre-training withwhole word masking for Chinese BERT[ J] . IEEE / ACMTransactions on Audio, Speech, and Language Processing, 2021, 29: 3504-3514.
[26] CUI Y M, YANG Z Q, LIU T. PERT: pre-trainingBERT with permuted language model[ EB / OL] . ( 2022 -03 - 14 ) [ 2023 - 06 - 11 ] . https: ∥ arxiv. org /abs/ 2203. 06906.
[27] CUI Y M, CHE W X, WANG S J, et al. LERT: a linguistically-motivated pre-trained language model [ EB /OL] . ( 2022 - 11 - 10) [ 2023 - 06 - 11] . https:∥arxiv.org / abs/ 2211. 05344.

Similar References:

Memo

Last Update: 2024-09-29