我已使用实体规则为社会保障号添加新标签。 即使设置了 overwrite_ents=true 但它仍然无法识别

我验证了正则表达式是正确的。不知道我还需要做什么 我之前尝试过=“ner”但结果相同

text = "My name is yuyyvb and I leave on 605 W Clinton Street. My social security 690-96-4032"
nlp = spacy.load("en_core_web_sm")
ruler = EntityRuler(nlp, overwrite_ents=True)
ruler.add_patterns([{"label": "SSN", "pattern": [{"TEXT": {"REGEX": r"\d{3}[^\w]\d{2}[^\w]\d{4}"}}]}])
nlp.add_pipe(ruler)
doc  = nlp(text)
for ent in doc.ents:
    print("{} {}".format(ent.text, ent.label_))

实际上，您拥有的 SSN 被 spacy 标记为 5 个块：

print([token.text for token in nlp("690-96-4032")])
# => ['690', '-', '96', '-', '4032']

因此，要么使用自定义分词器-数字之间的值不会拆分为单独的标记，或者 - 更简单 - 为连续 5 个标记创建一个模式：

patterns = [{"label": "SSN", "pattern": [{"TEXT": {"REGEX": r"^\d{3}$"}}, {"TEXT": "-"}, {"TEXT": {"REGEX": r"^\d{2}$"}}, {"TEXT": "-"}, {"TEXT": {"REGEX": r"^\d{4}$"}} ]}]

完整的 spacy 演示：

import spacy
from spacy.pipeline import EntityRuler

nlp = spacy.load("en_core_web_sm")
ruler = EntityRuler(nlp, overwrite_ents=True)
patterns = [{"label": "SSN", "pattern": [{"TEXT": {"REGEX": r"^\d{3}$"}}, {"TEXT": "-"}, {"TEXT": {"REGEX": r"^\d{2}$"}}, {"TEXT": "-"}, {"TEXT": {"REGEX": r"^\d{4}$"}} ]}]
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)

text = "My name is yuyyvb and I leave on 605 W Clinton Street. My social security 690-96-4032"
doc = nlp(text)
print([(ent.text, ent.label_) for ent in doc.ents])
# => [('605', 'CARDINAL'), ('690-96-4032', 'SSN')]

So, {"TEXT": {"REGEX": r"^\d{3}$"}}匹配仅由三位数字组成的令牌，{"TEXT": "-"} is a -字符等

用 spacy 覆盖连字符数字标记化

如果您对如何通过覆盖默认标记化来实现它感兴趣，请注意infixes https://github.com/explosion/spaCy/blob/58533f01bf926546337ad2868abe7fc8f0a3b3ae/spacy/lang/punctuation.py#L36: the r"(?<=[0-9])[+\-\*^](?=[0-9-])"正则表达式使 spacy 将连字符分隔的数字拆分为单独的标记。使1-2-3 and 1-2就像子字符串被标记为单个标记一样，删除-来自正则表达式。好吧，你不能这样做，这要棘手得多：你需要用 2 个正则表达式替换它：r"(?<=[0-9])[+*^](?=[0-9-])" and r"(?<=[0-9])-(?=-)"因为事实上-也在数字之间进行检查（(?<=[0-9])) 和一个连字符（参见(?=[0-9-])).

所以，整个事情看起来像

import spacy
from spacy.tokenizer import Tokenizer
from spacy.pipeline import EntityRuler
from spacy.util import compile_infix_regex

def custom_tokenizer(nlp):
    # Take out the existing rule and replace it with a custom one:
    inf = list(nlp.Defaults.infixes)
    inf.remove(r"(?<=[0-9])[+\-\*^](?=[0-9-])")
    inf = tuple(inf)
    infixes = inf + tuple([r"(?<=[0-9])[+*^](?=[0-9-])", r"(?<=[0-9])-(?=-)"]) 
    infix_re = compile_infix_regex(infixes)

    return Tokenizer(nlp.vocab, prefix_search=nlp.tokenizer.prefix_search,
                                suffix_search=nlp.tokenizer.suffix_search,
                                infix_finditer=infix_re.finditer,
                                token_match=nlp.tokenizer.token_match,
                                rules=nlp.Defaults.tokenizer_exceptions)

nlp = spacy.load("en_core_web_sm")
nlp.tokenizer = custom_tokenizer(nlp)
ruler = EntityRuler(nlp, overwrite_ents=True)
ruler.add_patterns([{"label": "SSN", "pattern": [{"TEXT": {"REGEX": r"^\d{3}\W\d{2}\W\d{4}$"}}]}])
nlp.add_pipe(ruler)

text = "My name is yuyyvb and I leave on 605 W Clinton Street. My social security 690-96-4032. Some 9---al"
doc = nlp(text)
print([t.text for t in doc])
# =>  ['My', 'name', 'is', 'yuyyvb', 'and', 'I', 'leave', 'on', '605', 'W', 'Clinton', 'Street', '.', 'My', 'social', 'security', '690-96-4032', '.', 'Some', '9', '-', '--al']
print([(ent.text, ent.label_) for ent in doc.ents])
# => [('605', 'CARDINAL'), ('690-96-4032', 'SSN'), ('9', 'CARDINAL')]

如果你遗漏了r"(?<=[0-9])-(?=-)", the ['9', '-', '--al']会变成'9---al'.

NOTE你需要使用^\d{3}\W\d{2}\W\d{4}$ regex: ^ and $匹配令牌的开始和结束（否则，部分匹配的令牌也将被识别为 SSN）并且[^\w]等于\W.