如何用Spacy用句子来分解文档

Question

如何将文档（例如，段落，书籍等）分解为句子。

例如，"The dog ran. The cat jumped"加入["The dog ran", "The cat jumped"]与spacy？

Answer 1

来自spacy's github support page

from __future__ import unicode_literals, print_function
from spacy.en import English

raw_text = 'Hello, world. Here are two sentences.'
nlp = English()
doc = nlp(raw_text)
sentences = [sent.string.strip() for sent in doc.sents]

Answer 2

答案

import spacy
nlp = spacy.load('en_core_web_sm')

text = 'My first birthday was great. My 2. was even better.'
sentences = [i for i in nlp(text).sents]

其他信息
假设您已经在系统上安装了模型“ en_core_web_sm”。如果没有，您可以通过在终端中运行以下命令来轻松安装它：

$ python -m spacy download en_core_web_sm

（有关所有可用模型的概述，请参见here。）

根据您的数据，这可能会比仅使用spacy.lang.en.English产生更好的结果。一个（非常简单的）比较示例：

import spacy
from spacy.lang.en import English

nlp_simple = English()
nlp_simple.add_pipe(nlp_simple.create_pipe('sentencizer'))

nlp_better = spacy.load('en_core_web_sm')


text = 'My first birthday was great. My 2. was even better.'

for nlp in [nlp_simple, nlp_better]:
    for i in nlp(text).sents:
        print(i)
    print('-' * 20)

输出：

>>> My first birthday was great.
>>> My 2.
>>> was even better.
>>> --------------------
>>> My first birthday was great.
>>> My 2. was even better.
>>> --------------------

Answer 3

在 spacy 3.0.1 中，他们改变了管道。

from spacy.lang.en import English 

nlp = English()
nlp.add_pipe('sentencizer')


def split_in_sentences(text):
    doc = nlp(text)
    return [str(sent).strip() for sent in doc.sents]

Answer 4

最新答案是这样：

from __future__ import unicode_literals, print_function
from spacy.lang.en import English # updated

raw_text = 'Hello, world. Here are two sentences.'
nlp = English()
nlp.add_pipe(nlp.create_pipe('sentencizer')) # updated
doc = nlp(raw_text)
sentences = [sent.string.strip() for sent in doc.sents]