用聚类与词嵌入提升生成数据的语义连贯性，大幅降低资源消耗。
CTG-KrEW: Generating Synthetic Structured Contextually Correlated Content by Conditional Tabular GAN with K-Means Clustering and Efficient Word Embedding
- 结合K均值聚类与高效词嵌入，生成语义关联的表格内容。
- 相比传统方法，减少99%的CPU时间与33%内存占用。
- 适合需要高质量合成数据的研究者与开发者使用。
条件表格生成对抗网络（CTGAN）及其衍生模型能高效灵活地生成合成表格数据，表现优异且适应性强。然而存在两大局限：一是无法保持语义相关词汇或短语的完整性；二是生成上下文关联内容时，生成内容语义浅薄，且训练阶段消耗大量内存和CPU时间。为解决这些问题，本文提出新框架CTGKrEW（基于K均值聚类与高效词嵌入的条件表格生成对抗网络），可生成属性由语义和上下文一致词汇组成的现实合成表格数据。该框架在真实自由职业平台Upwork的数据集上进行训练与评估。通过多维度实验分析生成数据的多样性、上下文相似性、频率分布与关联性，并验证系统可行性。CTGKrEW相较传统方法节省约99%的CPU时间，内存占用降低33%。此外，我们开发了名为KrEW的Web应用，支持技能信息的实时合成数据生成，地址为 https://riyasamanta.github.io/krew.html，向公众与研究社区免费开放。
原文摘要 · Abstract (English)
Conditional Tabular Generative Adversarial Networks (CTGAN) and their various derivatives are attractive for their ability to efficiently and flexibly create synthetic tabular data, showcasing strong performance and adaptability. However, there are certain critical limitations to such models. The first is their inability to preserve the semantic integrity of contextually correlated words or phrases. For instance, skillset in freelancer profiles is one such attribute where individual skills are semantically interconnected and indicative of specific domain interests or qualifications. The second challenge of traditional approaches is that, when applied to generate contextually correlated tabular content, besides generating semantically shallow content, they consume huge memory resources and CPU time during the training stage. To address these problems, we introduce a novel framework, CTGKrEW (Conditional Tabular GAN with KMeans Clustering and Word Embedding), which is adept at generating realistic synthetic tabular data where attributes are collections of semantically and contextually coherent words. CTGKrEW is trained and evaluated using a dataset from Upwork, a realworld freelancing platform. Comprehensive experiments were conducted to analyze the variability, contextual similarity, frequency distribution, and associativity of the generated data, along with testing the framework's system feasibility. CTGKrEW also takes around 99\% less CPU time and 33\% less memory footprints than the conventional approach. Furthermore, we developed KrEW, a web application to facilitate the generation of realistic data containing skill-related information. This application, available at https://riyasamanta.github.io/krew.html, is freely accessible to both the general public and the research community.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。