Tokenize words in a list of sentences Python

Question

i currently have a file that contains a list that is looks like

example = [\'Mary had a little lamb\' , 
           \'Jack went up the hill\' , 
           \'Ji         


        

Answer 1

i make this script to make all people understood how to tokenize, so they can build their Natural Language Processing's engine by them self.

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')

i make a different process, i restart the process from paragraph, to make everybody more understood of word processing. paragraph to process is:

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

tokenize paragraph to sentence:

sentence = token_to_sentence(example)

will result:

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

tokenize to words:

words = token_to_words(sentence)

will result:

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

i will explain how this work.

first, i used regex to search all word and spaces which separate the words and stop until found a punctuation, the regex is:

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

so the computation wil be took the words and spaces in bracket:

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

the result is still not clear, contain some 'None' characters. so i used this script to removed the 'None' characters:

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

so the paragraph will tokenize to sentence, but not clear sentence the result is:

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

as you see the result show some sentence start by a space. so to make a clear paragraph without starting a space, i make this regex:

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

it will make a clear sentence like:

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

so, we must make two process to make a good result.

the answer of your question is start from here...

to tokenize the sentence to words, i make the paragraph iteration and used regex just to capture the word while it was iterating with this regex:

([\w]{0,})

and clear the empty characters again with:

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

so the result is really clear only the list of words:

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

in the future to make a good NLP, you need to have your own phrase database and search if the phrase is in the sentence, after make a list of phrase, the rest of words is clear a word.

with this method, i can build my own NLP in my language (bahasa Indonesia) which really-really lack of module.

edited:

i don't see your question that want to compare the words. so you have another sentence to compare....i give you bonus not only bonus, i give you how to count it.

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

in this case the step you must do is: 1. iter the mod_example 2. compare the first sentence with the words from mod_example. 3. make some calculation

so the script will be:

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)