NLP Academy · 课时

词干提取：截取词根

将 running 和 runs 映射为 run

词干提取：截取词根 是 CoddyKit 上的免费 NLP Academy 课时。这是第 3 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 NLP Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 NLP Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

One Word, Many Forms

The verb run shows up as running, runs, and ran. To you they share a meaning, but raw text counts each one separately, splitting the signal. 🌱

Enter Stemming

Stemming chops the endings off a word to reach a rough root called the stem. The goal is to make related forms collapse into one shared token.

Rules, Not a Dictionary

A stemmer follows simple suffix rules instead of looking words up. It strips endings like ing, ed, and s, so it is fast but a bit crude.

The Porter Stemmer

NLTK ships the classic PorterStemmer. You create one instance, then call its stem method on any word you want reduced.

from nltk.stem import PorterStemmer
ps = PorterStemmer()
print(ps.stem('running'))

Forms Collapse Together

Feed in several forms of the same verb and they all reduce to the same stem. Now your counts can finally add up.

for w in ['running', 'runs', 'ran']:
    print(ps.stem(w))

Stems Are Not Real Words

Stemming is blunt. The word studies becomes studi, which is not a real word, but it still matches study and studied as one stem.

print(ps.stem('studies'))

Watch for Over-Stemming

Sometimes a stemmer cuts too far and merges unrelated words. This over-stemming can blur meanings, so always sanity-check the output on your text.

print(ps.stem('universal'))
print(ps.stem('university'))

Stem a Token List

In practice you stem every token in a document. A comprehension applies the stemmer across the whole list in one clean line.

words = ['cats', 'caring', 'cared']
print([ps.stem(w) for w in words])

Other Stemmers Exist

Porter is the default, but NLTK also offers the snappier SnowballStemmer, which supports many languages and fixes some Porter rough edges.

from nltk.stem import SnowballStemmer
sb = SnowballStemmer('english')
print(sb.stem('happily'))

Lowercase First

Stemmers expect lowercase input. Fold case before stemming, or Running and running may slip through as two different tokens again.

print(ps.stem('Running'.lower()))

When to Reach for It

Stemming trades precision for speed. It shines in search and large pipelines where a rough but fast match beats slow, exact analysis. ⚡

Quick Check

Let's lock in how stemming behaves.

Recap

You learned stemming chops suffixes with simple rules to collapse word forms into one stem. It is fast and rough, sometimes yielding non-words. Nice work! 🎉

常见问题解答

「词干提取：截取词根」课时是免费的吗？

是的 — 「词干提取：截取词根」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 NLP Academy 课程的其余内容，请升级到 CoddyKit PRO。 NLP Academy 课程共包含 4 节课。

「词干提取：截取词根」这节课中我会学到什么？

将 running 和 runs 映射为 run 你通过在浏览器中直接运行的动手代码来练习 NLP Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 NLP Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 NLP Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 3 节课，共 4 节。

「词干提取：截取词根」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 NLP Academy 课中编写并运行代码吗？

能。每节 NLP Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

← 返回 NLP Academy