流式主题模型中的词汇外处理和主题质量控制策略

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neurocomputing Pub Date : 2024-10-28 DOI:10.1016/j.neucom.2024.128757
Tung Nguyen , Tung Pham , Linh Ngo Van, Ha-Bang Ban, Khoat Than
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引用次数: 0

摘要

主题模型已成为分析流数据的普遍工具。然而,现有的流式主题模型在应用于现实世界的数据流时存在一些局限性。这包括无法在整个流式处理过程中适应不断发展的词汇表和控制话题质量。在本文中,我们提出了一种新颖的流式主题建模方法,可动态适应数据流不断变化的性质。我们的方法利用字节对编码嵌入(BPEmb)来解决因数据流中出现新词而产生的词汇不足问题。此外,我们还引入了一个主题变化变量,对主题的参数更新进行精细控制,并提出了一种保存方法,在每个时间步骤中保留高一致性主题,帮助保持语义质量。为了进一步提高模型的适应性,我们的方法允许根据需要动态调整主题空间的大小。据我们所知,我们是第一个在流式处理过程中解决词汇扩展和保持主题质量的方法。广泛的实验表明,我们的方法非常有效。
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Out-of-vocabulary handling and topic quality control strategies in streaming topic models
Topic models have become ubiquitous tools for analyzing streaming data. However, existing streaming topic models suffer from several limitations when applied to real-world data streams. This includes the inability to accommodate evolving vocabularies and control topic quality throughout the streaming process. In this paper, we propose a novel streaming topic modeling approach that dynamically adapts to the changing nature of data streams. Our method leverages Byte-Pair Encoding embedding (BPEmb) to resolve the out-of-vocabulary problem that arises with new words in the stream. Additionally, we introduce a topic change variable that provides fine-grained control over topics’ parameter updates and present a preservation approach to retain high-coherence topics at each time step, helping preserve semantic quality. To further enhance model adaptability, our method allows dynamical adjustment of topic space size as needed. To the best of our knowledge, we are the first to address the expansion of vocabulary and maintain topic quality during the streaming process. Extensive experiments show the superior effectiveness of our method.
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
期刊最新文献
Editorial Board Virtual sample generation for small sample learning: A survey, recent developments and future prospects Adaptive selection of spectral–spatial features for hyperspectral image classification using a modified-CBAM-based network FPGA-based component-wise LSTM training accelerator for neural granger causality analysis Multi-sensor information fusion in Internet of Vehicles based on deep learning: A review
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