问题描述

再见，

我正在尝试对连接的单词进行后处理，这些单词被标记为单独的标记，而它们本应是一个单独的标记。例如：

Example:

Sentence: "up-scaled"
Tokens: ['up', '-', 'scaled']
Expected: ['up-scaled']

目前，我的解决方案是使用匹配器：

matcher = Matcher(nlp.vocab)
pattern = [{'IS_ALPHA': True, 'IS_SPACE': False},
           {'ORTH': '-'},
           {'IS_ALPHA': True, 'IS_SPACE': False}]

matcher.add('HYPHENATED', None, pattern)

def quote_merger(doc):
    # this will be called on the Doc object in the pipeline
    matched_spans = []
    matches = matcher(doc)
    for match_id, start, end in matches:
        span = doc[start:end]
        matched_spans.append(span)
    for span in matched_spans:  # merge into one token after collecting all matches
        span.merge()
    #print(doc)
    return doc

nlp.add_pipe(quote_merger, first=True)  # add it right after the tokenizer
doc = nlp(text)

但是，这将导致以下预期问题：

Example 2:

Sentence: "I know I will be back - I had a very pleasant time"
Tokens: ['i', 'know', 'I', 'will', 'be', 'back - I', 'had', 'a', 'very', 'pleasant', 'time']
Expected: ['i', 'know', 'I', 'will', 'be', 'back', '-', 'I', 'had', 'a', 'very', 'pleasant', 'time']

非常感谢

编辑： 我已经尝试了发布的解决方案：Why does spaCy not preserve intra-word-hyphens during tokenization like Stanford CoreNLP does?

但是，我没有使用此解决方案，因为它导致带有撇号(‘)的单词和带有小数的数字的标记化错误：

Sentence: "It's"
Tokens: ["I", "t's"]
Expected: ["It", "'s"]

Sentence: "1.50"
Tokens: ["1", ".", "50"]
Expected: ["1.50"]

这就是我使用Matcher而不是尝试编辑正则表达式的原因。

推荐答案

匹配器实际上不是用于此的正确工具。您应该改为修改令牌化程序。

https://github.com/explosion/spaCy/blob/58533f01bf926546337ad2868abe7fc8f0a3b3ae/spacy/lang/punctuation.py#L37-L49

import spacy
from spacy.tokenizer import Tokenizer
from spacy.lang.char_classes import ALPHA, ALPHA_LOWER, ALPHA_UPPER, CONCAT_QUOTES, LIST_ELLIPSES, LIST_ICONS
from spacy.util import compile_infix_regex

def custom_tokenizer(nlp):
    infixes = (
        LIST_ELLIPSES
        + LIST_ICONS
        + [
            r"(?<=[0-9])[+-*^](?=[0-9-])",
            r"(?<=[{al}{q}]).(?=[{au}{q}])".format(
                al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES
            ),
            r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
            #r"(?<=[{a}])(?:{h})(?=[{a}])".format(a=ALPHA, h=HYPHENS),
            r"(?<=[{a}0-9])[:<>=/](?=[{a}])".format(a=ALPHA),
        ]
    )

    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")
nlp.tokenizer = custom_tokenizer(nlp)
print([t.text for t in nlp("It's 1.50, up-scaled haven't")])
# ['It', "'s", "'", '1.50', "'", ',', 'up-scaled', 'have', "n't"]

编辑以添加(因为这看起来确实不必要地复杂，而且您确实应该能够重新定义中缀模式，而无需加载整个新的自定义记号赋值器)：

如果您刚刚加载了模型(v2.1.8)，并且还没有调用nlp()，您也可以只替换infix_re.finditer，而不创建自定义标记器：

nlp = spacy.load('en')
nlp.tokenizer.infix_finditer = infix_re.finditer

有一个缓存错误，有望在V2.2中得到修复，使其在任何时候都能正确工作，而不仅仅是在新加载的模型上。(否则这种行为非常令人困惑，这就是为什么在2.1.8版中创建自定义记号赋值器是更好的通用建议。)

这篇关于连字词的空格标记化的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持编程学习网！