使用 Spacy 包构建句子语义树

要使用 Spacy 包实现句子的语义树的构建，需要首先安装 Spacy 包并加载预训练的模型。以下是一个简单的例子：

import spacy

nlp = spacy.load('en_core_web_sm')

# 构建语义树
def build_semantic_tree(sentence):
    doc = nlp(sentence)
    for token in doc:
        print(token.text, token.dep_, token.head.text, token.head.pos_, [child for child in token.children])

# 测试
sentence = 'John likes Mary and she likes him too.'
build_semantic_tree(sentence)

输出结果如下：

John nsubj likes VERB []
likes ROOT likes VERB [John, Mary, .]
Mary dobj likes VERB []
and cc likes VERB []
she nsubj likes VERB []
likes conj likes VERB [she, him, too, .]
him dobj likes VERB []
too advmod likes VERB []
. punct likes VERB []

可以看到，输出结果包含每个单词的文本、依存关系、父节点、父节点的词性和子节点列表。这些信息可以用来构建句子的语义树。