如何在模式中使用模式（嵌套模式）编写规则

Question

我想在 Spacy 中编写一个匹配文本的模式，然后可以选择文本右侧的所有括号（如果有的话）。

例如，对于以下文本，我将箭头“->”的左侧部分作为输入，并将箭头的右侧部分作为输出。

However, we found that This Example (TE (as explained above) other stuff) -> This Example (TE (as explained above) other stuff)

Here is another example This Example (TE (some text here)) -> This Example (TE (some text here))

Some random text and then This Example (TE) -> This Example (TE)

Then a final example This Example -> This Example

这些文本不固定，仅作为示例。

我希望得到与结尾一样多的起始括号（甚至可能没有括号模式）

我尝试了以下方法，但它不起作用。

patt_1 = [
    {"OP": "+", "LOWER": {"IN": ['this','example']}},
    {"OP": "*", "LOWER": {"IN":["("]}},
    {"OP": "*", "POS": {"IN":["ADJ", "PROPN", "NOUN", "CCONJ", "NUM", "ADV"]}},
    {"OP": "*", "LOWER": {"IN": [")"]}}
]

它只匹配第一个括号。由于我对长文本进行操作，如果末尾有右括号，我还想匹配一个短语而不是整个文档。

我想要类似下面的东西，但它不起作用......

patt_1 = [
{"OP": "+", "LOWER": {"IN":["("]}},
{"OP": "+", "POS": {"IN":["ADJ", "PROPN", "NOUN"]}},
{"OP": "+", "LOWER": {"IN": [")"]}}
]
patt_2 = [
{"OP": "+", "LOWER": {"IN":["("]}},
{"OP": "+", "POS": {"IN":["ADJ", "PROPN", "NOUN"]}},
{"OP": "+", "LOWER": {"IN":["("]}},
{"OP": "+", "POS": {"IN":["ADJ", "PROPN", "NOUN"]}},
{"OP": "+", "LOWER": {"IN": [")"]}},
{"OP": "+", "POS": {"IN":["ADJ", "PROPN", "NOUN"]}},
{"OP": "+", "LOWER": {"IN": [")"]}}
]

patt_3 = [
    {"OP": "+", "LOWER": {"IN": ['this','example']}},
    { "OP": "*", patt_1},
    { "OP": "*", patt_2}
]

感谢您的帮助

Answer 1

如果所有文本都具有相同的模式，则可以使用正则表达式来识别正确的部分。

import re

text = "However, we found that This Example (TE (as explained above) other stuff)"
cleaned_text = re.sub(r"However, we found that ", "", text).strip()
print(cleaned_text) # Outputs "This Example (TE (as explained above) other stuff)"

编辑（见 cmets）：

specific_phrase = "This Example"
example_document = "However, we found that This Example (TE (as explained above) other stuff)"
brackets_regex = r" \(.*\)"

if re.search(specific_phrase, example_document) and re.search(brackets_regex, example_document):
    matched_pattern = specific_phrase + re.search(brackets_regex, example_document).group()
print(matched_pattern) # Output: This Example (TE (as explained above) other stuff)

然后可以将其放入一个函数中，然后循环遍历您的文档以处理它们。