在句子列表中标记单词Python

Question

我目前有一个包含类似

的列表的文件

example = ['Mary had a little lamb' , 
           'Jack went up the hill' , 
           'Jill followed suit' ,    
           'i woke up suddenly' ,
           'it was a really bad dream...']

“example”是这样的句子的列表，我希望输出看起来像：

mod_example = ["'Mary' 'had' 'a' 'little' 'lamb'" , 'Jack' 'went' 'up' 'the' 'hill' ....] 等等。 我需要将每个单词标记为单独的句子，以便我可以将mod_example（一次使用for循环）的句子中的每个单词与参考句子进行比较。

我试过了：

for sentence in example:
    text3 = sentence.split()
    print text3 

并得到以下输出：

['it', 'was', 'a', 'really', 'bad', 'dream...']

如何为所有句子获取此内容？ 它会一直覆盖。是的，还要提一下我的方法是否正确？ 这应该是一个单词列表，标记为tokenized .. thanks

Answer 1

您可以在NLTK（http://nltk.org/api/nltk.tokenize.html）中使用带有列表理解的单词tokenizer，请参阅http://docs.python.org/2/tutorial/datastructures.html#list-comprehensions

>>> from nltk.tokenize import word_tokenize
>>> example = ['Mary had a little lamb' , 
...            'Jack went up the hill' , 
...            'Jill followed suit' ,    
...            'i woke up suddenly' ,
...            'it was a really bad dream...']
>>> tokenized_sents = [word_tokenize(i) for i in example]
>>> for i in tokenized_sents:
...     print i
... 
['Mary', 'had', 'a', 'little', 'lamb']
['Jack', 'went', 'up', 'the', 'hill']
['Jill', 'followed', 'suit']
['i', 'woke', 'up', 'suddenly']
['it', 'was', 'a', 'really', 'bad', 'dream', '...']

Answer 2

我制作这个脚本是为了让所有人都了解如何标记化，因此他们可以自己构建自己的自然语言处理引擎。

import re
from contextlib import redirect_stdout
from io import StringIO

example = 'Mary had a little lamb, Jack went up the hill, Jill followed suit, i woke up suddenly, it was a really bad dream...'

def token_to_sentence(str):
    f = StringIO()
    with redirect_stdout(f):
        regex_of_sentence = re.findall('([\w\s]{0,})[^\w\s]', str)
        regex_of_sentence = [x for x in regex_of_sentence if x is not '']
        for i in regex_of_sentence:
            print(i)
        first_step_to_sentence = (f.getvalue()).split('\n')
    g = StringIO()
    with redirect_stdout(g):
        for i in first_step_to_sentence:
            try:
                regex_to_clear_sentence = re.search('\s([\w\s]{0,})', i)
                print(regex_to_clear_sentence.group(1))
            except:
                print(i)
        sentence = (g.getvalue()).split('\n')
    return sentence

def token_to_words(str):
    f = StringIO()
    with redirect_stdout(f):
        for i in str:
            regex_of_word = re.findall('([\w]{0,})', i)
            regex_of_word = [x for x in regex_of_word if x is not '']
            for word in regex_of_word:
                print(regex_of_word)
        words = (f.getvalue()).split('\n')

我进行了不同的处理，我从段落重新开始了该处理，以使每个人都对文字处理有所了解。要处理的段落是：

example = 'Mary had a little lamb, Jack went up the hill, Jill followed suit, i woke up suddenly, it was a really bad dream...'

将段落标记为句子：

sentence = token_to_sentence(example)

将得到：

['Mary had a little lamb', 'Jack went up the hill', 'Jill followed suit', 'i woke up suddenly', 'it was a really bad dream']

代名词：

words = token_to_words(sentence)

将得到：

['Mary', 'had', 'a', 'little', 'lamb', 'Jack', 'went, 'up', 'the', 'hill', 'Jill', 'followed', 'suit', 'i', 'woke', 'up', 'suddenly', 'it', 'was', 'a', 'really', 'bad', 'dream']

我将解释其工作原理。

首先，我使用正则表达式搜索所有单词和将单词分隔开的空格，直到找到标点为止，正则表达式为：

([\w\s]{0,})[^\w\s]{0,}

因此计算将使用括号中的单词和空格：

'(Mary had a little lamb),( Jack went up the hill, Jill followed suit),( i woke up suddenly),( it was a really bad dream)...'

结果仍然不清楚，包含一些“无”字符。所以我用这个脚本删除了'None'字符：

[x for x in regex_of_sentence if x is not '']

因此该段落将标记为句子，但不清楚句子的结果是：

['Mary had a little lamb', ' Jack went up the hill', ' Jill followed suit', ' i woke up suddenly', ' it was a really bad dream']

如您所见，结果显示一些句子以空格开头。因此，为了使一个清晰的段落而没有一个空格，我做了这个正则表达式：

\s([\w\s]{0,})

它会说一个清晰的句子，例如：

['Mary had a little lamb', 'Jack went up the hill', 'Jill followed suit', 'i woke up suddenly', 'it was a really bad dream']

所以，我们必须进行两个过程才能取得良好的结果。

您问题的答案是从这里开始...

要将句子标记为单词，我进行了段落迭代，并使用正则表达式只是在正则表达式进行迭代时捕获单词：

([\w]{0,})

并使用以下命令再次清除空白字符：

[x for x in regex_of_word if x is not '']

所以结果实际上只清除了单词列表：

['Mary', 'had', 'a', 'little', 'lamb', 'Jack', 'went, 'up', 'the', 'hill', 'Jill', 'followed', 'suit', 'i', 'woke', 'up', 'suddenly', 'it', 'was', 'a', 'really', 'bad', 'dream']

为了将来成为一个好的NLP，您需要拥有自己的短语数据库并搜索短语中是否包含该短语，在列出短语列表之后，其余单词将清除一个单词。

使用这种方法，我可以用我的语言（印度尼西亚语）构建我自己的NLP，而这实际上是缺少模块的。

编辑：

我看不到您想要比较两个字的问题。因此，您还有另一句话可以比较...。我不仅给您奖金，还给您如何计算。

mod_example = ["'Mary' 'had' 'a' 'little' 'lamb'" , 'Jack' 'went' 'up' 'the' 'hill']

在这种情况下，您必须执行的步骤是： 1.修改mod_example 2.将第一句与mod_example中的单词进行比较。 3.做一些计算

因此脚本将是：

import re
from contextlib import redirect_stdout
from io import StringIO

example = 'Mary had a little lamb, Jack went up the hill, Jill followed suit, i woke up suddenly, it was a really bad dream...'
mod_example = ["'Mary' 'had' 'a' 'little' 'lamb'" , 'Jack' 'went' 'up' 'the' 'hill']

def token_to_sentence(str):
    f = StringIO()
    with redirect_stdout(f):
        regex_of_sentence = re.findall('([\w\s]{0,})[^\w\s]', str)
        regex_of_sentence = [x for x in regex_of_sentence if x is not '']
        for i in regex_of_sentence:
            print(i)
        first_step_to_sentence = (f.getvalue()).split('\n')
    g = StringIO()
    with redirect_stdout(g):
        for i in first_step_to_sentence:
            try:
                regex_to_clear_sentence = re.search('\s([\w\s]{0,})', i)
                print(regex_to_clear_sentence.group(1))
            except:
                print(i)
        sentence = (g.getvalue()).split('\n')
    return sentence

def token_to_words(str):
    f = StringIO()
    with redirect_stdout(f):
        for i in str:
            regex_of_word = re.findall('([\w]{0,})', i)
            regex_of_word = [x for x in regex_of_word if x is not '']
            for word in regex_of_word:
                print(regex_of_word)
        words = (f.getvalue()).split('\n')

def convert_to_words(str):
    sentences = token_to_sentence(str)
    for i in sentences:
        word = token_to_words(i)
    return word

def compare_list_of_words__to_another_list_of_words(from_strA, to_strB):
        fromA = list(set(from_strA))
        for word_to_match in fromA:
            totalB = len(to_strB)
            number_of_match = (to_strB).count(word_to_match)
            data = str((((to_strB).count(word_to_match))/totalB)*100)
            print('words: -- ' + word_to_match + ' --' + '\n'
            '       number of match    : ' + number_of_match + ' from ' + str(totalB) + '\n'
            '       percent of match   : ' + data + ' percent')



#prepare already make, now we will use it. The process start with script below:

if __name__ == '__main__':
    #tokenize paragraph in example to sentence:
    getsentences = token_to_sentence(example)

    #tokenize sentence to words (sentences in getsentences)
    getwords = token_to_words(getsentences)

    #compare list of word in (getwords) with list of words in mod_example
    compare_list_of_words__to_another_list_of_words(getwords, mod_example)

Answer 3

细分列表“示例”

first_split = []

for i in example:

    first_split.append(i.split())

细分first_split列表中的元素

second_split = []

for j in first_split:

    for k in j:

        second_split.append(k.split())

分解second_split列表的元素并将其附加到最终列表，以了解编码人员如何输出输出

final_list = []

for m in second_split:

    for n in m:

        if(n not in final_list):

            final_list.append(n)

print(final_list)   

Answer 4

对我而言，很难说，你想要做什么。

这个怎么样

exclude = set(['Mary', 'Jack', 'Jill', 'i', 'it'])

mod_example = []
for sentence in example:
    words = sentence.split()
    # Optionally sort out some words
    for word in words:
        if word in exclude:
            words.remove(word)
    mod_example.append('\'' + '\' \''.join(words) + '\'')

print mod_example

哪个输出

["'had' 'a' 'little' 'lamb'", "'went' 'up' 'the' 'hill'", "'followed' 'suit'", 
"'woke' 'up' 'suddenly'", "'was' 'a' 'really' 'bad' 'dream...'"]
>>> 

编辑： 另一个建议基于OP提供的进一步信息

example = ['Area1 Area1 street one, 4454 hikoland' ,
           'Area2 street 2, 52432 hikoland, area2' ,
           'Area3 ave three, 0534 hikoland' ]

mod_example = []
for sentence in example:
    words = sentence.split()
    # Sort out some words
    col1 = words[0]
    col2 = words[1:]
    if col1 in col2:
        col2.remove(col1)
    elif col1.lower() in col2:
        col2.remove(col1.lower())
    mod_example.append(col1 + ': ' + ' '.join(col2))

输出

>>>> print mod_example
['Area1: street one, 4454 hikoland', 'Area2: street 2, 52432 hikoland,', 
'Area3: ave three, 0534 hikoland']
>>> 

Answer 5

你可以使用nltk（作为@alvas suggests）和一个递归函数，它接受任何对象并将每个str标记为：

from nltk.tokenize import word_tokenize
def tokenize(obj):
    if obj is None:
        return None
    elif isinstance(obj, str): # basestring in python 2.7
        return word_tokenize(obj)
    elif isinstance(obj, list):
        return [tokenize(i) for i in obj]
    else:
        return obj # Or throw an exception, or parse a dict...

用法：

data = [["Lorem ipsum dolor. Sit amet?", "Hello World!", None], ["a"], "Hi!", None, ""]
print(tokenize(data))

输出：

[[['Lorem', 'ipsum', 'dolor', '.', 'Sit', 'amet', '?'], ['Hello', 'World', '!'], None], [['a']], ['Hi', '!'], None, []]

Answer 6

在Spacy中，它非常简单：

import spacy

example = ['Mary had a little lamb' , 
           'Jack went up the hill' , 
           'Jill followed suit' ,    
           'i woke up suddenly' ,
           'it was a really bad dream...']

nlp = spacy.load("en_core_web_sm")

result = []

for line in example:
    sent = nlp(line)
    token_result = []
    for token in sent:
        token_result.append(token)
    result.append(token_result)

print(result)

输出将是：

[[Mary, had, a, little, lamb], [Jack, went, up, the, hill], [Jill, followed, suit], [i, woke, up, suddenly], [it, was, a, really, bad, dream, ...]]

Answer 7

这也可以通过pytorch torchtext as

完成

from torchtext.data import get_tokenizer

tokenizer = get_tokenizer('basic_english')
example = ['Mary had a little lamb' , 
            'Jack went up the hill' , 
            'Jill followed suit' ,    
            'i woke up suddenly' ,
            'it was a really bad dream...']
tokens = []
for s in example:
    tokens += tokenizer(s)
# ['mary', 'had', 'a', 'little', 'lamb', 'jack', 'went', 'up', 'the', 'hill', 'jill', 'followed', 'suit', 'i', 'woke', 'up', 'suddenly', 'it', 'was', 'a', 'really', 'bad', 'dream', '.', '.', '.']