Different Text Cleaning Methods for NLP Tasks

May 4, 2020 · 5 min read · python programming NLP data science ·

Cleaning text for natural language processing (NLP) tasks is an important step that can help improve the performance of your model. In this blog post, we will discuss some common text cleaning techniques and how to apply them to your text data.

The first step in cleaning text for NLP is to remove any noisy or irrelevant information. This can include things like HTML tags, URLs, and other extraneous characters. Removing this information can help the model focus on the relevant content and improve its performance.

Another common text cleaning step is to standardize the text. This can include things like converting the text to lowercase, removing punctuation, and expanding abbreviations. Standardizing the text can help the model better understand the content and improve its performance.

Another important text cleaning step is to remove stop words. Stop words are words that are commonly used in a language but do not convey any meaning, such as "the," "and," and "but." Removing stop words can help the model focus on the important content and improve its performance.

Once you have cleaned your text data, it is important to check that it is in the correct format for your model. For example, many NLP models expect the input text to be tokenized, which means breaking the text into individual words or phrases. If your text is not already tokenized, you will need to tokenize it before feeding it to the model.

Here are some examples of text cleaning methods with code samples:

Removing HTML tags

To remove HTML tags from text data, you can use the BeautifulSoup library in Python.

1from bs4 import BeautifulSoup
2
3html_text = "<p>This is some text with <strong>HTML</strong> tags.</p>"
4
5# Use BeautifulSoup to remove the HTML tags
6soup = BeautifulSoup(html_text, 'html.parser')
7cleaned_text = soup.get_text()
8
9print(cleaned_text)  # Output: This is some text with HTML tags.

Removing punctuation

To remove punctuation from text data, you can use the string library in Python.

 1import string
 2
 3text = "This is some text with punctuation.!"
 4
 5# Use the string.punctuation property to get a string of all punctuation characters
 6punctuation = string.punctuation
 7
 8# Use the translate() method to remove the punctuation from the text
 9cleaned_text = text.translate(str.maketrans('', '', punctuation))
10
11print(cleaned_text)  # Output: This is some text with punctuation

Removing stop words

To remove stop words from text data, you can use the nltk library in Python.

 1import nltk
 2from nltk.corpus import stopwords
 3
 4text = "This is some text with stop words."
 5
 6# Use the nltk.corpus.stopwords.words() method to get a list of stop words
 7stop_words = stopwords.words('english')
 8
 9# Use a list comprehension to remove the stop words from the text
10cleaned_text = [word for word in text.split() if word not in stop_words]
11
12print(cleaned_text)  # Output: ['This', 'text', 'stop', 'words.']

Removing numbers

To remove numbers from text data, you can use a regular expression.

1import re
2
3text = "This is some text with 1234 numbers."
4
5# Use the re.sub() method to remove any sequences of digits from the text
6cleaned_text = re.sub(r'\d+', '', text)
7
8print(cleaned_text)  # Output: This is some text with  numbers.

Standardizing case

To standardize the case of text data, you can use the str.lower() method in Python.

1text = "This is some text with MIXED CASE."
2
3# Use the str.lower() method to convert the text to lowercase
4cleaned_text = text.lower()
5
6print(cleaned_text)  # Output: this is some text with mixed case.

Stemming words

To stem words in text data, you can use the nltk library in Python.

 1import nltk
 2from nltk.stem import PorterStemmer
 3
 4text = "This is some text with stemming words."
 5
 6# Use the PorterStemmer from the nltk.stem module to stem the words in the text
 7stemmer = PorterStemmer()
 8stemmed_words = [stemmer.stem(word) for word in text.split()]
 9
10# Join the stemmed words into a single string
11cleaned_text = ' '.join(stemmed_words)
12
13print(cleaned_text)  # Output: this is some text with stem word.

Removing whitespace

To remove whitespace from text data, you can use the str.strip() method in Python.

1text = " This is some text with whitespace.  "
2
3# Use the str.strip() method to remove any leading or trailing whitespace
4cleaned_text = text.strip()
5
6print(cleaned_text)  # Output: This is some text with whitespace.

Removing accents

To remove accents from text data, you can use the unidecode library in Python.

1from unidecode import unidecode
2
3text = "This is some text with accented characters: é, í, ó, ú, ñ"
4
5# Use the unidecode.unidecode() method to remove the accents from the text
6cleaned_text = unidecode(text)
7
8print(cleaned_text)  # Output: This is some text with accented characters: e, i, o, u, n

Removing special characters

To remove special characters from text data, you can use a regular expression.

1import re
2
3text = "This is some text with special characters: !@#$%^&*()_+"
4
5# Use the re.sub() method to remove any non-alphanumeric characters from the text
6cleaned_text = re.sub(r'[^a-zA-Z0-9\s]', '', text)
7
8print(cleaned_text)  # Output: This is some text with special characters

Removing newline characters

To remove newline characters from text data, you can use the str.replace() method in Python.

1text = "This is some text\nwith newline characters\n"
2
3# Use the str.replace() method to replace newline characters with a space
4cleaned_text = text.replace('\n', ' ')
5
6print(cleaned_text)  # Output: This is some text with newline characters

Removing duplicates

To remove duplicate words from text data, you can use the set() function in Python. Here is an example:

1text = "This is some text with duplicate words. Words words words."
2
3# Use the set() function to remove duplicate words from the text
4cleaned_text = ' '.join(set(text.split()))
5
6print(cleaned_text)  # Output: This is some text with duplicate words.

These are just a few more examples of text cleaning methods. There are many other techniques that you can use, depending on the specific requirements of your task.

Author: Sadman Kabir Soumik