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English(EN) TaxCE : A Framework for Automated Taxonomy Construction and Evaluation at Scale

新框架大规模自动化分类法构建和评估

一个名为TaxCE的新框架已被开发出来,用于从大量非结构化文本数据中自动构建和评估分层分类法。该框架通过渐进式内容压缩、语义单元去重和自下而上组织主题,解决了现有方法的局限性。TaxCE还引入了三个新颖的评估指标——排他性、穷尽性和粒度(EEG),并将它们整合到迭代优化过程中以提高分类法的质量。实验表明,TaxCE在当前的主题建模、神经网络和基于LLM的方法方面表现显著优于它们,人类评估也证实了其卓越的可操作性和导航性。 AI

影响 该框架可以显著改善大型组织构建和分析用户反馈的方式,从而获得更具可操作性的见解。

排序理由 该集群包含一篇研究论文,详细介绍了一个用于特定NLP任务的新框架和评估指标。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新框架大规模自动化分类法构建和评估

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该集群包含一篇研究论文,详细介绍了一个用于特定NLP任务的新框架和评估指标。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Sandeep Sricharan Mukku, Albert Aristotle Nanda, Rohit Pyati ·

    TaxCE:大规模自动化分类法构建与评估框架

    arXiv:2608.30614v1 Announce Type: new Abstract: Organizing unstructured feedback text into hierarchical taxonomy is a fundamental challenge in NLP, particularly in domains where feedback arrives at massive scale in varied forms such as reviews, transcripts, and surveys. Existing …