python - 使用 unicode 撇号正确标记英语缩写

Question

如果 unicode apostrophes，您如何修改默认的 spacy (v3.0.5) 标记器以正确拆分英语缩写(不是 ' )被使用。

import spacy

nlp = spacy.load('en_core_web_sm')
apostrophes = ["'",'\u02B9', '\u02BB', '\u02BC', '\u02BD', '\u02C8', '\u02CA', '\u02CB', '\u0060', '\u00B4']
for apo in apostrophes:
    text = f"don{apo}t"
    print([t for t in nlp(text)])
>>> 
 [do, n't]
 [donʹt]
 [donʻt]
 [donʼt]
 [donʽt]
 [donˈt]
 [donˊt]
 [donˋt]
 [don`t]
 [don´t]

所有示例所需的输出为 [do, n't]我最好的猜测是使用所有可能的撇号变体来扩展默认的 tokenizer_exceptions。但这不起作用，因为 Tokenizer 特殊情况不允许修改文本。

import spacy 
from spacy.util import compile_prefix_regex, compile_suffix_regex, compile_infix_regex

nlp = spacy.load('en_core_web_sm')

apostrophes = ['\u02B9', '\u02BB', '\u02BC', '\u02BD', '\u02C8', '\u02CA', '\u02CB', '\u0060', '\u00B4']
default_rules = nlp.Defaults.tokenizer_exceptions
extended_rules = default_rules.copy()
for key, val in default_rules.items():
    if "'" in key:
        for apo in apostrophes:
            extended_rules[key.replace("'", apo)] = val

rules = nlp.Defaults.tokenizer_exceptions
infix_re = compile_infix_regex(nlp.Defaults.infixes)
prefix_re = compile_prefix_regex(nlp.Defaults.prefixes)
suffix_re = compile_suffix_regex(nlp.Defaults.suffixes)

nlp.tokenizer =  spacy.tokenizer.Tokenizer(
        nlp.vocab,
        rules = extended_rules,
        prefix_search=prefix_re.search,
        suffix_search=suffix_re.search,
        infix_finditer=infix_re.finditer,
    )
            
apostrophes = ["'",'\u02B9', '\u02BB', '\u02BC', '\u02BD', '\u02C8', '\u02CA', '\u02CB', '\u0060', '\u00B4']
for apo in apostrophes:
    text = f"don{apo}t"
    print([t for t in nlp(text)])

>>> ValueError: [E997] Tokenizer special cases are not allowed to modify the text. This would map ':`(' to ':'(' given token attributes '[{65: ":'("}]'.

Answer 1

您只需要添加一个异常(exception)而不更改文本。

import spacy 

nlp = spacy.load('en_core_web_sm')

from spacy.attrs import ORTH, NORM
case = [{ORTH: "do"}, {ORTH: "n`t", NORM: "not"}]
tokenizer = nlp.tokenizer
tokenizer.add_special_case("don`t", case)

doc =  nlp("I don`t believe in bugs")

print(list(doc))
# => [I, do, n`t, believe, in, bugs]

如果要更改文本，则应将其作为预处理步骤进行。