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English(EN) The Art of Hierarchical Competing Patterns: Gaussian Process Optimization of Hyphenation

高斯过程优化自动化断字模式生成

研究人员开发了一种使用高斯过程贝叶斯优化自动生成断字模式的方法,这是文本处理系统的一个关键组成部分。该方法将 patgen 程序参数配置文件的选择构建为一个黑盒超参数优化问题。优化目标平衡了精度(以 F_{1/7} 分数衡量)与 trie 大小和准确性之间的权衡。在 14 种语言的 17 个数据集上的评估表明,与手动调整的配置文件相比,GP 优化的配置文件显著提高了 F_{1/7} 分数并减小了 trie 大小,使得模式生成更具可重复性,并且减少了对专家调整的依赖。 AI

影响 自动化复杂的文本处理任务,有望提高排版和渲染引擎的效率和可重复性。

排序理由 该集群包含一篇学术论文,详细介绍了使用高斯过程贝叶斯优化来优化断字模式的新方法。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

高斯过程优化自动化断字模式生成

本文如何被排名

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该集群包含一篇学术论文,详细介绍了使用高斯过程贝叶斯优化来优化断字模式的新方法。[lever_c_demoted from research: ic=1 ai=0.4]
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paper, other
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完整方法见我们的编辑标准

报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ond\v{r}ej Sojka (Faculty of Informatics, Masaryk University), Petr Sojka (Faculty of Informatics, Masaryk University) ·

    分词的Gaussian过程优化:分层竞争模式的艺术

    arXiv:2609.07638v1 Announce Type: cross Abstract: Hyphenation patterns remain a compact and widely deployed solution for word breaking in typesetting systems, text processors, and web rendering engines, but their generation still depends on manually tuned patgen program parameter…