计算机辅助设计与图形学学报

面向多属性文档的精细化语义模式交互探索方法

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刘陈一 , 李杰 ^* , 沈天舒

计算机辅助设计与图形学学报 | 可视化与可视分析 2025,37(12): 2131-2146

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计算机辅助设计与图形学学报 |可视化与可视分析 2025 , 37 (12) : 2131 -2146

面向多属性文档的精细化语义模式交互探索方法

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沈天舒（1998—），女，硕士研究生，主要研究方向为主题模型可视化.

刘陈一, 李杰^*, 沈天舒

作者信息

天津大学智能与计算学部　天津　300350

通讯作者:

（jie.li@tju.edu.cn）

作者简介:

刘陈一（2003—），男，在校学生，主要研究方向为机器学习

李杰（1984—），男，博士，副教授，硕士生导师，CCF会员，论文通信作者，主要研究方向为机器学习、大数据可视分析；

沈天舒（1998—），女，硕士研究生，主要研究方向为主题模型可视化.

Refined Semantic Pattern Interaction Exploration for Multi-Attribute Documents: A Study on Subspace Topic Modeling and Visual Analysis

Chenyi Liu, Jie Li^*, Tianshu Shen

Affiliations

College of Intelligence and Computing, Tianjin University, Tianjin　300350

出版时间: 2025-12-20 doi: 10.3724/SP.J.1089.2024-00108

文章导航

摘要

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现有的语义模式探索方式限制了对全局语义模式的理解，少有研究关注保留上下文语义的精细化语义模式探索. 文中通过深度结合机器学习和可视化技术，运用面向多属性文档的精细化语义模式可视分析方法，使用户能够在灵活地分析不同属性下的精细化语义模式的同时感知全局语义模式. 首先引入表征重构网络，得到包含多属性文档语义和属性信息的潜在向量，使主题模型能够更好地识别子空间主题；然后引入人在回路的语义模式可视分析方法，开发了一套包括探索管理器、子空间投影仪和主题解释器的语义模式可视化系统，支持用户选择表征重构的属性子空间，交互探索语义模式，并提供分析结果. 基于游戏评论数据集、美国新闻数据集和特朗普大选数据集，采用主题多样性和主题一致性的指标与既有方法进行对比，实验结果表明，所提出的主题模型在主题建模上具有较好的泛用性和灵活性；用户实验包括语义模式探索任务，验证了所提方法和可视化系统在语义模式探索上的出色执行效率和有效性.

关键词

语义模式 / 多属性文档 / 子空间主题建模 / 可视分析 / 交互探索

Abstract

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Existing approaches to semantic pattern exploration limit the comprehension of global semantic patterns, with few studies focusing on the fine-grained semantic pattern exploration that preserves contextual semantics. In this paper, this paper combines machine learning and visualization techniques in depth, employing a fine-grained semantic pattern visual analysis method for multi-attribute documents. This allows users to flexibly analyze fine-grained semantic patterns under different attributes while perceiving global semantic patterns. Firstly, this paper introduces a representation reconstruction network to obtain latent vectors that contain semantic and attribute information of multi-attribute documents, enabling the topic model to better identify subspace topics. Then, this paper introduces a human-in-the-loop semantic pattern visual analysis method and develop a semantic pattern visualization system that includes an exploration manager, a subspace projector, and a topic interpreter. This system supports users in selecting attribute subspaces for representation reconstruction, interactively exploring semantic patterns, and providing analysis results. Based on game review datasets, US news datasets, and Trump election datasets, this paper compare our proposed topic model with existing methods using topic diversity and topic coherence metrics. Experimental results show that the proposed topic model has good generalizability and flexibility in topic modeling. User experiments including semantic pattern exploration tasks verify the outstanding efficiency and effectiveness of the proposed method and visualization system in semantic pattern exploration.

Key words

semantic patterns / multi-attribute documents / subspace topic modeling / visual analysis / interactive exploration

引用本文

刘陈一, 李杰, 沈天舒. 面向多属性文档的精细化语义模式交互探索方法. 计算机辅助设计与图形学学报, 2025 , 37 (12) : 2131 -2146 . DOI: 10.3724/SP.J.1089.2024-00108

Chenyi Liu, Jie Li, Tianshu Shen. Refined Semantic Pattern Interaction Exploration for Multi-Attribute Documents: A Study on Subspace Topic Modeling and Visual Analysis[J]. Journal of Computer-Aided Design & Computer Graphics, 2025 , 37 (12) : 2131 -2146 . DOI: 10.3724/SP.J.1089.2024-00108

基金

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国家自然科学基金(62372321)

参考文献

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文献

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参考文献引证文献

排序方式：

[1]

Li J, Chen S M, Andrienko G L, et al. Visual exploration of spatial and temporal variations of tweet topic popularity[C]//Proceedings of the EuroVis Workshop on Visual Analytics. Aire-la-Ville: Eurographics Association Press, 2018: 7-11

[2]

Felix C, Pandey A V, Bertini E, et al. RevEx: visual investigative journalism with a million healthcare reviews[OL]. [2024-03-01]. http://cj2015.brown.columbia.edu/papers/revex.pdf

[3]

Li J, Chen S M, Chen W, et al. Semantics-space-time cube: a conceptual framework for systematic analysis of texts in space and time[J]. IEEE Transactions on Visualization and Computer Graphics, 2020, 26(4): 1789-1806

[4]

Aggarwal C C, Zhai C X. A survey of text classification algorithms[M]//Aggarwal C C, Zhai C X. Mining Text Data. New York: Springer, 2012: 163-222

[5]

Sparck Jones K. A statistical interpretation of term specificity and its application in retrieval[J]. Journal of Documentation, 1972, 28(5): 11-21

[6]

Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation[J]. The Journal of Machine Learning Research, 2003, 3: 993-1022

[7]

Deerwester S, Dumais S T, Furnas G W, et al. Indexing by latent semantic analysis[J]. Journal of the American Society for Information Science, 1990, 41(6): 391-407

[8]

Xu W, Liu X, Gong Y H. Document clustering based on non-negative matrix factorization[C]//Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Press, 2003: 267-273

[9]

Blei D M, Jordan M I, Griffiths T L, et al. Hierarchical topic models and the nested Chinese restaurant process[C]//Proceedings of the 16th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2003: 17-24

[10]

The Y W, Jordan M I, Beal M J, et al. Sharing clusters among related groups: hierarchical Dirichlet processes[C]//Proceedings of the 17th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2004: 1385-1392

[11]

Lacoste-Julien S, Sha F, Jordan M I. DiscLDA: discriminative learning for dimensionality reduction and classification[C]//Proceedings of the 21st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc, 2008: 897-904

[12]

Blei D M, Mcauliffe J D. Supervised topic models[C]//Proceedings of the 20th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc, 2007: 121-128

[13]

Liu Y, Liu Z Y, Chua T S, et al. Topical word embeddings[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2015: 2418-2424

[14]

Qiang J P, Chen P, Wang T, et al. Topic modeling over short texts by incorporating word embeddings[C]//Proceedings of the 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. Heidelberg: Springer, 2017: 363-374

[15]

Angelov D. Top2Vec: distributed representations of topics[OL]. [2024-03-01]. https://arxiv.org/abs/2008.09470

[16]

Grootendorst M. BERTopic: neural topic modeling with a class-based TF-IDF procedure[OL]. [2024-03-01]. https://arxiv.org/abs/2203.05794

[17]

Egger R, Yu J. A topic modeling comparison between LDA, NMF, Top2Vec, and BERTopic to demystify Twitter posts[J]. Frontiers in Sociology, 2022, 7: Article No.886498

[18]

Song X L, Wang X T, Hu X H. Semantic pattern mining for text mining[C]//Proceedings of the IEEE International Conference on Big Data (Big Data). Los Alamitos: IEEE Computer Society Press, 2016: 150-155

[19]

Naïm H, Aznag M, Durand N, et al. Semantic pattern mining based web service recommendation[C]//Proceedings of the 14th International Conference on Service-Oriented Computing. Heidelberg: Springer, 2016: 417-432

[20]

Zhang C, Han J W, Shou L D, et al. Splitter: mining fine-grained sequential patterns in semantic trajectories[J]. Proceedings of the VLDB Endowment, 2014, 7(9): 769-780

[21]

Cao N, Lin Y R, Sun X H, et al. Whisper: tracing the spatiotemporal process of information diffusion in real time[J]. IEEE Transactions on Visualization and Computer Graphics, 2012, 18(12): 2649-2658

[22]

Hu M D, Wongsuphasawat K, Stasko J. Visualizing social media content with sententree[J]. IEEE Transactions on Visualization and Computer Graphics, 2017, 23(1): 621-630

[23]

Alexander E, Kohlmann J, Valenza R, et al. Serendip: topic model-driven visual exploration of text corpora[C]//Proceedings of the IEEE Conference on Visual Analytics Science and Technology. Los Alamitos: IEEE Computer Society Press, 2014: 173-182

[24]

Dou W W, Wang X Y, Chang R, et al. ParallelTopics: a probabilistic approach to exploring document collections[C]//Proceedings of the IEEE Conference on Visual Analytics Science and Technology. Los Alamitos: IEEE Computer Society Press, 2011: 231-240

[25]

Cui W W, Liu S X, Wu Z F, et al. How hierarchical topics evolve in large text corpora[J]. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 2281-2290

[26]

Liu S X, Wang X T, Chen J F, et al. TopicPanorama: a full picture of relevant topics[C]//Proceedings of the IEEE Conference on Visual Analytics Science and Technology. Los Alamitos: IEEE Computer Society Press, 2014: 183-192

[27]

Kim M, Kang K, Park D, et al. TopicLens: efficient multi-level visual topic exploration of large-scale document collections[J]. IEEE Transactions on Visualization and Computer Graphics, 2017, 23(1): 151-160

[28]

Bengio Y, Ducharme R, Vincent P. A neural probabilistic language model[C]//Proceedings of the 13th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2000: 893-899

[29]

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. Red Hook: Curran Associates Inc, 2013: 3111-3119

[30]

Le Q, Mikolov T. Distributed representations of sentences and documents[OL]. [2024-03-01]. https://doi.org/10.48550/arXiv.1405.4053

[31]

Lau J H, Baldwin T. An empirical evaluation of doc2vec with practical insights into document embedding generation[OL]. [2024-03-01]. https://doi.org/10.48550/arXiv.1607.05368

[32]

Devlin J, Chang M W, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1. Minneapolis: Association for Computational Linguistics, 2019: 4171-4186

[33]

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. Red Hook: Curran Associates Inc, 2017: 6000-6010

[34]

Reimers N, Gurevych I. Sentence-BERT: sentence embeddings using Siamese BERT-networks[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Hong Kong: Association for Computational Linguistics, 2019: 3982-3992

[35]

Chicco D. Siamese neural networks: an overview[M]//Cartwright H. Artificial Neural Networks. New York: Springer, 2021: 73-94

[36]

McInnes L, Healy J, Melville J. UMAP: uniform manifold approximation and projection for dimension reduction[OL]. [2024-03-01]. https://arxiv.org/abs/1802.03426

[37]

Hu Y N, Boyd-Graber J, Satinoff B, et al. Interactive topic modeling[J]. Machine Learning, 2014, 95(3): 423-469

[38]

El-Assady M, Sperrle F, Deussen O, et al. Visual analytics for topic model optimization based on user-steerable speculative execution[J]. IEEE Transactions on Visualization and Computer Graphics, 2019, 25(1): 374-384

[39]

Park D, Kim S, Lee J, et al. ConceptVector: text visual analytics via interactive lexicon building using word embedding[J]. IEEE Transactions on Visualization and Computer Graphics, 2018, 24(1): 361-370

[40]

Choo J, Lee C, Reddy C K, et al. UTOPIAN: user-driven topic modeling based on interactive nonnegative matrix factorization[J]. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(12): 1992-2001

[41]

El-Assady M, Kehlbeck R, Collins C, et al. Semantic concept spaces: guided topic model refinement using word-embedding projections[J]. IEEE Transactions on Visualization and Computer Graphics, 2019, 26(1): 1001-1011

[42]

Dou W W, Yu L, Wang X Y, et al. HierarchicalTopics: visually exploring large text collections using topic hierarchies[J]. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(12): 2002-2011

[43]

Smith A, Kumar V, Boyd-Graber J, et al. Closing the loop: user-centered design and evaluation of a human-in-the-loop topic modeling system[C]//Proceedings of the 23rd International Conference on Intelligent User Interfaces. New York: ACM Press, 2018: 293-304

[44]

Beeferman D, Jiang H. Topic-time heatmaps for human-in-the-loop topic detection and tracking[OL]. [2024-03-01]. https://arxiv.org/pdf/2110.07337

[45]

Khan M H U R R, Wakabayashi K. Reducing human effort in keyphrase-based human-in-the-loop topic models: a method for keyphrase recommendations[C]//Proceedings of the 25th International Conference on Information Integration and Web Intelligence. Heidelberg: Springer, 2023: 233-248

[46]

Johnson B, Shneiderman B. Tree-maps: a space-filling approach to the visualization of hierarchical information structures[C]//Proceedings of the Visualization. Los Alamitos: IEEE Computer Society Press, 1991: 284-291

[47]

Long L K, Hui L C, Fook G Y, et al. A study on the effectiveness of tree-maps as tree visualization techniques[J]. Procedia Computer Science, 2017, 124: 108-115

[48]

Barlow T, Neville P. A comparison of 2-D visualizations of hierarchies[C]//Proceedings of the IEEE Symposium on Information Visualization. Los Alamitos: IEEE Computer Society Press, 2001: 131-138

[49]

Cai G, Sun F, Sha Y Z. Interactive visualization for topic model curation[OL]. [2024-03-01]. https://ceur-ws.org/Vol-2068/esida5.pdf

[50]

Jiang X Y, Zhang J W. A text visualization method for cross-domain research topic mining[J]. Journal of Visualization, 2016, 19(3): 561-576

[51]

Shao L, Schleicher T, Behrisch M, et al. Guiding the exploration of scatter plot data using motif-based interest measures[J]. Journal of Visual Languages & Computing, 2016, 36: 1-12

[52]

Chen H S, Wang X M, Pan S R, et al. Identify topic relations in scientific literature using topic modeling[J]. IEEE Transactions on Engineering Management, 2021, 68(5): 1232-1244

[53]

El-Assady M, Sevastjanova R, Sperrle F, et al. Progressive learning of topic modeling parameters: a visual analytics framework[J]. IEEE Transactions on Visualization and Computer Graphics, 2018, 24(1): 382-391

[54]

Xie T, Qin P, Zhu L B. Study on the topic mining and dynamic visualization in view of LDA model[J]. Modern Applied Science, 2018, 13(1): Article No.204

[55]

Zou C Y, Hou D Q. LDA analyzer: a tool for exploring topic models[C]//Proceedings of the IEEE International Conference on Software Maintenance and Evolution. Los Alamitos: IEEE Computer Society Press, 2014: 593-596

[56]

Roberts J C. State of the art: coordinated & multiple views in exploratory visualization[C]//Proceedings of the 5th International Conference on Coordinated and Multiple Views in Exploratory Visualization. Los Alamitos: IEEE Computer Society Press, 2007: 61-71

[57]

Xie P, Tao W Y, Li J, et al. Exploring multi-dimensional data via subset embedding[J]. Computer Graphics Forum, 2021, 40(3): 75-86

[58]

Li J, Zhou C Q. Incorporation of human knowledge into data embeddings to improve pattern significance and interpretability[J]. IEEE Transactions on Visualization and Computer Graphics, 2022, 29(1): 723-733

[59]

MacQueen J. Some methods of classification and analysis of multivariate observations[C]//Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press, 1967: 281-297

[60]

Bostock M, Ogievetsky V, Heer J. D³ data-driven documents[J]. IEEE Transactions on Visualization and Computer Graphics, 2011, 17(12): 2301-2309

[61]

Abadi M, Agarwal A, Barham P, et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems[OL]. [2024-03-01]. https://arxiv.org/abs/1603.04467

[62]

Marko M. Steam reviews dataset 2021[EB/OL]. [2024-03-01]. https://www.kaggle.com/datasets/najzeko/steam-reviews-2021

[63]

All the news[EB/OL]. [2024-03-01]. https://components.one/datasets/all-the-news-articles-dataset/

[64]

Manch H. US election 2020 tweets[EB/OL]. [2024-03-01]. https://www.kaggle.com/datasets/manchunhui/us-election-2020-tweets

[65]

Clark L A, Watson D. Constructing validity: basic issues in objective scale development[M]//Kazdin A E. Methodological Issues and Strategies in Clinical Research. 4th ed. Washington: American Psychological Association, 2016: 187-203

[66]

Mimno D, Wallach H, Talley E, et al. Optimizing semantic coherence in topic models[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Edinburgh: ACL, 2011: 262-272

[67]

BBC news classification[EB/OL]. [2024-03-01]. https://www.kaggle.com/competitions/learn-ai-bbc/data

[68]

Anshul C, Raisinghani M. Airline reviews[EB/OL]. [2024-03-01]. https://www.kaggle.com/datasets/chaudharyanshul/airline-reviews

[69]

Hamner B. Hillary Clinton and Donald trump tweets[EB/OL]. [2024-03-01]. https://www.kaggle.com/datasets/benhamner/clinton-trump-tweets

[70]

The Devastator. Hate speech and offensive language detection[EB/OL]. [2024-03-01]. https://www.kaggle.com/datasets/thedevastator/hate-speech-and-offensive-language-detection

[71]

Chen S, Li S H, Chen S M, et al. R-Map: a map metaphor for visualizing information reposting process in social media[J]. IEEE Transactions on Visualization and Computer Graphics, 2020, 26(1): 1204-1214

[72]

Yang H, Li J, Chen S M. TopicRefiner: coherence-guided steerable LDA for visual topic enhancement[J]. IEEE Transactions on Visualization and Computer Graphics, 2024, 30(8): 4542-4557

2025年第37卷第12期

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doi: 10.3724/SP.J.1089.2024-00108

接收时间：2024-03-01
首发时间：2026-10-10
出版时间：2025-12-20

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收稿日期：2024-03-01
修回日期：2024-09-04

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国家自然科学基金(62372321)

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天津大学智能与计算学部　天津　300350

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（jie.li@tju.edu.cn）

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https://castjournals.cast.org.cn/joweb/jsjfzsj/CN/10.3724/SP.J.1089.2024-00108

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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