Python中的NLP信息提取（spaCy）

Question

Python中的NLP信息提取（spaCy）

9

我正在尝试从以下段落结构中提取此类信息：

 women_ran men_ran kids_ran walked
         1       2        1      3
         2       4        3      1
         3       6        5      2

text = ["On Tuesday, one women ran on the street while 2 men ran and 1 child ran on the sidewalk. Also, there were 3 people walking.", "One person was walking yesterday, but there were 2 women running as well as 4 men and 3 kids running.", "The other day, there were three women running and also 6 men and 5 kids running on the sidewalk. Also, there were 2 people walking in the park."]

我正在使用Python的spaCy作为我的自然语言处理库。我对NLP工作比较新，希望能得到一些指导，了解从这些句子中提取表格信息的最佳方法。

如果只是识别是否有人在跑步或散步，我会使用sklearn来拟合分类模型，但我需要提取的信息显然更加细致（我正在尝试检索每个子类别和其值）。任何指导都将不胜感激。

- kathystehl

1个回答

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可以查看英文原文，
原文链接

- syllogism_ · Accepted Answer

你需要使用依赖解析来完成这个任务。你可以通过displaCy可视化工具查看你的示例句子。

你可以用几种不同的方法来实现需要的规则，就像编写XPath查询、DOM选择器等时总有多种方式一样。

以下代码应该能够实现此功能：

nlp = spacy.load('en')
docs = [nlp(t) for t in text]
for i, doc in enumerate(docs):
    for j, sent in enumerate(doc.sents):
        subjects = [w for w in sent if w.dep_ == 'nsubj']
        for subject in subjects:
            numbers = [w for w in subject.lefts if w.dep_ == 'nummod']
            if len(numbers) == 1:
                print('document.sentence: {}.{}, subject: {}, action: {}, numbers: {}'.format(i, j, subject.text, subject.head.text, numbers[0].text))

对于您在文本中的示例，您应该获得：

document.sentence: 0.0, subject: men, action: ran, numbers: 2
document.sentence: 0.0, subject: child, action: ran, numbers: 1
document.sentence: 0.1, subject: people, action: walking, numbers: 3
document.sentence: 1.0, subject: person, action: walking, numbers: One