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使用 spaCy 为词语添加标签

读取 POS 与详细标签

使用 spaCy 为词语添加标签 是 CoddyKit 上的免费 NLP Academy 课时。这是第 2 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。

spaCy Tags for You

You do not have to label words by hand. spaCy reads a sentence and assigns a part of speech to every token automatically. ⚡

Load a Model

First load a trained model. The small English model en_core_web_sm knows how to tag, parse, and recognize entities right away.

import spacy
nlp = spacy.load("en_core_web_sm")

Process Your Text

Call nlp() on a string to get a Doc. The Doc holds every token along with the tags spaCy has already worked out for you.

doc = nlp("The cat sleeps quietly")

Loop Over Tokens

A Doc is iterable, so you can walk through it token by token. Each token is a rich object carrying its text and grammatical info.

for token in doc:
    print(token.text)

Read the Coarse Tag

Every token has a pos_ attribute holding its universal tag, such as NOUN or VERB. The trailing underscore gives you the readable string.

for token in doc:
    print(token.text, token.pos_)

The Fine-Grained Tag

For more detail, read token.tag_. It gives a precise label like VBZ for a present-tense verb, beyond the broad pos_ category.

print(doc[2].text, doc[2].tag_)

Explain Any Tag

Forgot what a code means? Call spacy.explain() with a tag and it returns a plain-English description you can read instantly.

print(spacy.explain("VBZ"))

Tags Come From Context

spaCy decides each tag from the whole sentence, not a lookup table. That is why book can be tagged NOUN or VERB depending on how you use it.

Filter by Part of Speech

Once tags exist, filtering is easy. Keep only tokens whose pos_ equals VERB to collect every action word in the text.

verbs = [t.text for t in doc if t.pos_ == "VERB"]

Tagging Is Fast

spaCy tags thousands of words per second, so you can run it over large documents without waiting. It is built for real production work.

One Model, Many Tasks

The same loaded model also handles parsing and entities. Tagging is just one part of spaCy's full pipeline running on each Doc.

Quick Check

Recall how spaCy exposes part-of-speech labels.

Recap

You loaded a model, processed text, and read both pos_ and tag_ for each token. spaCy turns raw sentences into tagged words in just a few lines. 🎉

常见问题解答

「使用 spaCy 为词语添加标签」课时是免费的吗？

是的 — 「使用 spaCy 为词语添加标签」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。

「使用 spaCy 为词语添加标签」这节课中我会学到什么？

读取 POS 与详细标签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 2 节课，共 4 节。

「使用 spaCy 为词语添加标签」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。

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