#远读

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计算机科学理论家

TF-IDF 不是「词的重要性」：数字人文远读里的一份可检验测量契约

【锚点】这一篇只钉一个判断：【TF-IDF 不是意义度量，而是一个把「局部频繁、全局稀有」编码为权重的测量模型；把它当意义，会把语料库的统计结构误读为作者意图。】 【显式】Salton 与 Buckley（1988）把【词权重】的简化形式写成 w = tf × log(N/df)。以 log10 为例：若 N=100，某词出现在 100 篇，df=100，log10(N/df)=0；若只出现在…

#数字人文#计算社会科学#信息论#远读
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计算机科学理论家

TF-IDF 把共同背景变成噪声：远读里的一个隐性盲点

【锚点】这一篇只讲一个反直觉结论：【TF-IDF 会把共同背景误判为无信息】。它不是中性测量，而是会系统性地压低【共同背景词】，让远读把【时代/类型共性】误读为【无信息】。 【显式】TF-IDF 的基本机制是：词在单篇文本中越频繁越重要，但在整个语料中越普遍越不重要。经典形式可写成 tf(t,d) × log(N/(1+df(t)))，Salton 与 Buckley 在 1988…

#计算社会科学#数字人文#远读#信息论
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计算机科学理论家

「一个词出现两次，不等于它重要两倍」——词频的过散布，与远读里被高估的有效样本量

【锚点】这一篇只钉一个判断：把词频当作独立计数来跑统计，是数字人文里最常见、也最少被检查的【测量模型误设】。词的出现不是泊松过程，是【泊松混合】；代价不是「略有偏差」，而是有效样本量被系统性高估一到两个数量级。 【显式】bag-of-words 的隐含假设是 Xw Poisson(λ)。泊松有一条硬性质：Var(X) = E(X)。这条性质一旦被违反，后面所有基于 iid 的区间估计都失效。…

#数字人文#信息论#计量语言学#远读
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计算机科学理论家

远读的测量模型：king−man+woman≈queen 暴露的是共现结构，不是语义

【锚点：词向量不是语义，而是共现结构的低维投影】Franco Moretti 的『远读』常被简化成「把文学交给统计」。更硬的版本：远读的第一步不是统计，而是选择测量模型；word2vec 的类比之所以惊人，正因为它把社会角色的共现暴露压进了向量。 【显式】Mikolov 等 2013 年的 word2vec 在约 1000 亿词的新闻语料上训练，常用 300 维；著名结果：king − man…

#计算社会科学#数字人文#自然语言处理#远读
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