依存句法分析基础
了解词语如何在句子中建立联系
依存句法分析基础 是 CoddyKit 上的免费 NLP Academy 课时。 这是第 4 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。
Beyond Single Tags
POS tags name each word's job, but not how words connect. Dependency parsing reveals which word depends on which, building a sentence map. 🔗
Sentences as Trees
A parser arranges words into a tree. One word becomes the root, and every other word hangs off it through a labeled link.
The Root Verb
The root is usually the main verb that anchors the sentence. In The cat chased the mouse, the root is chased.
Heads and Children
Every word has a head, the word it attaches to. The words attached to it are its children, forming the branches of the tree.
Read the Relation
Each link has a label, the dependency relation. A subject is tagged nsubj, while a direct object is tagged dobj.
spaCy Parses Free
The same model that tags words also parses them. After nlp(), every token already carries its dependency information, ready to read.
doc = nlp("The cat chased the mouse")Read dep_ and head
Use token.dep_ for the relation and token.head for the word it attaches to. Together they describe the token's place in the tree.
for t in doc:
print(t.text, t.dep_, t.head.text)Find the Subject
To get who does the action, look for the token whose dep_ equals nsubj. That word is the grammatical subject of the verb.
subj = [t.text for t in doc if t.dep_ == "nsubj"]Explain a Relation
Unsure what a label means? Pass it to spacy.explain() and get a clear description, just like you did for POS tags.
print(spacy.explain("dobj"))Visualize the Tree
Render the parse with displacy. Set the style to dep and it draws labeled arrows from each head to its children.
from spacy import displacy
displacy.render(doc, style="dep")Why Parsing Matters
Dependencies unlock real meaning, like linking an action to its actor and target. This structure powers extraction, search, and question answering.
Quick Check
Recall how subjects are marked in a parse.
Recap
You saw sentences as trees of heads and children, read dep_ in spaCy, and drew the parse with displacy. Now you can map how words relate. 🎉
常见问题解答
「依存句法分析基础」课时是免费的吗？
是的 — 「依存句法分析基础」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。
「依存句法分析基础」这节课中我会学到什么？
了解词语如何在句子中建立联系 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 4 节课，共 4 节。
「依存句法分析基础」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。