是否可以将spacy与已标记化的输入一起使用? [英] Is it possible to use spacy with already tokenized input?

问题描述

我有一个已经被标记成单词的句子.我想为句子中的每个单词获取语音标签的一部分.当我查看SpaCy中的文档时，我意识到它始于原始句子.我不想这样做，因为在这种情况下，spacy可能会以不同的标记化结束.因此，我想知道是否可以将spaCy与单词列表(而不是字符串)一起使用?

I have a sentence that has already been tokenized into words. I want to get the part of speech tag for each word in the sentence. When I check the documentation in SpaCy I realized it starts with the raw sentence. I don't want to do that because in that case, the spacy might end up with a different tokenization. Therefore, I wonder if using spaCy with the list of words (rather than a string) is possible or not ?

以下是有关我的问题的示例:

Here is an example about my question:

# I know that it does the following sucessfully :
import spacy
nlp = spacy.load('en_core_web_sm')
raw_text = 'Hello, world.'
doc = nlp(raw_text)
for token in doc:
    print(token.pos_)

但是我想做类似以下的事情:

But I want to do something similar to the following:

import spacy
nlp = spacy.load('en_core_web_sm')
tokenized_text = ['Hello',',','world','.']
doc = nlp(tokenized_text)
for token in doc:
    print(token.pos_)

我知道这是行不通的，但是有可能做类似的事情吗?

I know, it doesn't work, but is it possible to do something similar to that ?

推荐答案

您可以通过使用自己的替换spaCy的默认标记生成器来做到这一点:

You can do this by replacing spaCy's default tokenizer with your own:

nlp.tokenizer = custom_tokenizer

custom_tokenizer是将原始文本作为输入并返回Doc对象的函数.

Where custom_tokenizer is a function taking raw text as input and returning a Doc object.

您未指定获取令牌列表的方式.如果您已经有一个接受原始文本并返回令牌列表的函数，则对其进行一些小的更改:

You did not specify how you got the list of tokens. If you already have a function that takes raw text and returns a list of tokens, just make a small change to it:

def custom_tokenizer(text):
    tokens = []

    # your existing code to fill the list with tokens

    # replace this line:
    return tokens

    # with this:
    return Doc(nlp.vocab, tokens)

请参见Doc上的文档.

如果由于某种原因您无法执行此操作(也许您无权使用令牌化功能)，则可以使用字典:

If for some reason you cannot do this (maybe you don't have access to the tokenization function), you can use a dictionary:

tokens_dict = {'Hello, world.': ['Hello', ',', 'world', '.']}

def custom_tokenizer(text):
    if text in tokens_dict:
        return Doc(nlp.vocab, tokens_dict[text])
    else:
        raise ValueError('No tokenization available for input.')

无论哪种方式，您都可以像在第一个示例中那样使用管道:

Either way, you can then use the pipeline as in your first example:

doc = nlp('Hello, world.')

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