练习

B.1 假设下表给出了每个词出现在正面或负面电影评论中的似然，而且两类的先验概率相等。

朴素贝叶斯会把句子“I always like foreign films.”分到哪个类别？

B.2 给定以下简短电影评论，每篇都标有喜剧（comedy）或动作（action）类别：

1. fun, couple, love, love — comedy

2. fast, furious, shoot — action

3. couple, fly, fast, fun, fun — comedy

4. furious, shoot, shoot, fun — action

5. fly, fast, shoot, love — action

以及新文档 $D$：

fast, couple, shoot, fly

计算 $D$ 最可能的类别。假设使用朴素贝叶斯分类器，并对似然使用加一平滑。

B.3 针对下表给出的关键情感词文档计数及正负类别，分别训练多项式朴素贝叶斯和二值朴素贝叶斯模型；两个模型都使用加一平滑。

用两个朴素贝叶斯模型为下面的句子分配类别（pos 或 neg）：

A good, good plot and great characters, but poor acting.

回忆前文：在朴素贝叶斯文本分类中，任何从未在训练文档中出现的词都直接忽略（丢弃）。但不要丢弃只在某些类别出现、而未在其他类别出现的词；这正是加一平滑要解决的问题。两个模型的结论一致还是不同？

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