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使用 spaCy 提取实体

列出实体及其标签

使用 spaCy 提取实体 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 Does the Work

spaCy ships a trained NER component that finds entities for you. You feed it text and read back labeled spans. 🐍

Load a Model

First load a pipeline that includes NER. The small English model en_core_web_sm is a quick, friendly starting point.

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

Process Your Text

Pass a string into the pipeline to get a Doc object. The Doc holds tokens, sentences, and the entities spaCy found.

doc = nlp("Sundar Pichai leads Google in California.")

The ents Property

Every Doc exposes doc.ents, a tuple of detected entities. Loop over it to inspect each one individually.

for ent in doc.ents:
    print(ent)

Read the Text

Each entity has a .text attribute holding the exact words matched. This is the raw span lifted from your sentence.

print(ent.text)  # e.g. Sundar Pichai

Read the Label

Use .label_ (with the trailing underscore) to get a readable label string like PERSON, ORG, or GPE.

print(ent.label_)  # e.g. PERSON

Putting It Together

One small loop prints both the text and label of every entity, turning a sentence into clean structured data.

for ent in doc.ents:
    print(ent.text, ent.label_)

Character Positions

Entities also expose start_char and end_char, the offsets in the original string. Handy for highlighting matches later.

print(ent.start_char, ent.end_char)

Explain a Label

Forgot what GPE means? Call spacy.explain for a plain-English description of any entity label.

spacy.explain("GPE")  # 'Countries, cities, states'

Entities Are Spans

Each entity is a Span, a slice of the Doc. So it behaves like a mini-document you can further inspect token by token.

Collect Into a List

A quick comprehension gathers every entity into a list of tuples, ready to save, count, or feed into a report.

results = [(e.text, e.label_) for e in doc.ents]

Quick Check

Which attribute gives the readable entity label?

Recap

Load a model, run nlp() on text, then loop doc.ents reading .text and .label_. That is core NER in spaCy. ✅

常见问题解答

「使用 spaCy 提取实体」课时是免费的吗？

是的 — 「使用 spaCy 提取实体」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 提取实体」这节课中我会学到什么？

列出实体及其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 提取实体」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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