python - NLP - Python 中的信息提取 (spaCy)

Question

我试图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 NLP 库。我是 NLP 工作的新手，希望就什么是从此类句子中提取表格信息的最佳方式提供一些指导。

如果只是简单地识别是否有人运行或行走，我会使用 sklearn 来拟合分类模型，但我需要提取的信息显然比那(我正在尝试检索每个子类别和值)。任何指导将不胜感激。

Answer 1

为此你需要使用依赖解析。您可以使用 the displaCy visualiser 查看示例句子的可视化效果.

您可以通过几种不同的方式实现您需要的规则——就像总是有多种方式来编写 XPath 查询、DOM 选择器等一样。

像这样的东西应该可以工作:

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 text 中的示例，您应该得到:

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