python - 如何使用 Spacy 解析动词

Question

我正在尝试解析语料库中的动词并将它们列在字典中，并计算每个动词作为及物、不及物和双及物出现的次数。我想知道如何使用 spacy 来解析动词并将它们标记为及物、不及物和双及物。

Answer 1

这里我总结一下Mirith/Verb-categorizer中的代码。基本上，您可以循环遍历 VERB 标记并查看其子标记，将它们分类为及物、不及物或双及物。示例如下。

首先，导入spacy，

import spacy
nlp = spacy.load('en')

假设您有一个 token 示例，

tokens = nlp('I like this dog. It is pretty good. I saw a bird. We arrived at the classroom door with only seven seconds to spare.')

您可以创建以下函数将 VERB 转换为您想要的新类型:

def check_verb(token):
    """Check verb type given spacy token"""
    if token.pos_ == 'VERB':
        indirect_object = False
        direct_object = False
        for item in token.children:
            if(item.dep_ == "iobj" or item.dep_ == "pobj"):
                indirect_object = True
            if (item.dep_ == "dobj" or item.dep_ == "dative"):
                direct_object = True
        if indirect_object and direct_object:
            return 'DITRANVERB'
        elif direct_object and not indirect_object:
            return 'TRANVERB'
        elif not direct_object and not indirect_object:
            return 'INTRANVERB'
        else:
            return 'VERB'
    else:
        return token.pos_

示例

[check_verb(t) for t in tokens] # ['PRON', 'TRAN', 'DET', 'NOUN', 'PUNCT', ...]