如何使用Spacy按句子分解文档

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"到["The dog ran", "The cat jumped"] 与spacy？

Answer 1

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 2

回答

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]

附加信息
这假设您已经在您的系统上安装了模型“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，请参见此处。）

根据您的数据，这可以带来比仅使用spacy.lang.en.English. 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

最新的答案是这样的：

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]

Answer 4

从spacy的github支持页面

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]