Adding Space between Potential Words using Python

When working with text data processing, it is not uncommon to encounter strings where potential words are merged together without any spaces. This issue can arise due to a variety of factors such as errors in optical character recognition (OCR), missing delimiters during data extraction, or other data-related problems. In such scenarios, it becomes necessary to devise a method that can intelligently separate these potential words and restore the appropriate spacing. In this blog post, we will delve into the process of adding spaces between potential words using the power of Python programming.

Approach

We will adopt a machine learning-based approach to tackle this challenge. Our solution will involve leveraging a pre-trained language model called spaCy, which is a popular Python library offering extensive support for various natural language processing tasks. By utilizing the capabilities of spaCy, we can take advantage of functionalities such as tokenization, named entity recognition, and part-of-speech tagging.

Step 1: Installation

Before we begin, it is necessary to install the spaCy library. To do so, open your terminal or command prompt and execute the following command −

pip install spacy

Step 2: Downloading the Language Model

To utilize spaCy effectively, we need to download a specific language model that supports tokenization. In this example, we will utilize the English language model. Download the model by running the following command −

python -m spacy download en_core_web_sm

Step 3: Adding Spaces

Now that we have spaCy and the required language model installed, we can start writing our Python code. The following code snippet demonstrates the process of adding spaces between potential words −

import spacy

def add_spaces(text):
   nlp = spacy.load('en_core_web_sm')
   doc = nlp(text)
   words = []
   for token in doc:
      if not token.is_space:
         words.append(token.text)
      else:
         words.append(' ')
   return ''.join(words)

# Example usage
input_text = "Thisisatestsentencewithnospaces."
output_text = add_spaces(input_text)
print(output_text)

In the provided code snippet, we define a function named add_spaces, which accepts a string text as its input. Within the function, we load the English language model using spacy.load('en_core_web_sm'). Next, we process the input text using the nlp object, which applies various linguistic analyses to the text. We then iterate over the individual tokens in the processed document, checking if each token is a space character or not. If a token is not a space, we add its text to the words list. However, if a token is a space, we add an actual space character to the list instead of the token text. Finally, we join all the elements in the words list to obtain the output text with properly added spaces.

Handling Punctuation

When adding spaces between potential words, it's essential to handle punctuation marks that are adjacent to concatenated words. Without proper handling, the punctuation marks might disrupt the word separation. To address this, we can add spaces before and after punctuation marks to ensure they are properly separated from the words. To handle punctuation, we can utilize the string module in Python, which provides a string of all punctuation characters. By checking if a token matches any punctuation character, we can add spaces accordingly.

Here's the code snippet that handles punctuation −

import string

def add_spaces(text):
   nlp = spacy.load('en_core_web_sm')
   doc = nlp(text)
   words = []
   for token in doc:
      if not token.is_space:
         # Add space before punctuation marks
         if token.text in string.punctuation:
            words.append(' ')
         words.append(token.text)
         # Add space after punctuation marks
         if token.text in string.punctuation:
            words.append(' ')
      else:
         words.append(' ')
   return ''.join(words)

Handling Numeric Values

When dealing with concatenated words that include numeric values, it's important to handle these values appropriately to maintain their integrity. Without proper handling, numeric values might be incorrectly separated or merged with other words.

To handle numeric values, we can check if a token consists entirely of digits using the isdigit() method. If a token is a numeric value, we can add spaces before and after it to ensure proper separation from other words.

Here's the code snippet that handles numeric values −

def add_spaces(text):
   nlp = spacy.load('en_core_web_sm')
   doc = nlp(text)
   words = []
   for token in doc:
      if not token.is_space:
         # Add space before numeric values
         if token.text.isdigit():
            words.append(' ')
         words.append(token.text)
         # Add space after numeric values
         if token.text.isdigit():
            words.append(' ')
      else:
         words.append(' ')
   return ''.join(words)

In the code above, within the add_spaces function, we iterate over the tokens in the processed document. If a token is not a space, we check if it consists only of digits using token.text.isdigit(). If it does, we add a space before and after the numeric value to ensure proper separation.

Handling Acronyms and Abbreviations

When dealing with concatenated words, there may be instances where acronyms or abbreviations are embedded within them. It's important to handle these cases appropriately to preserve the intended meaning and maintain the correct separation of words.

To handle acronyms and abbreviations, we can leverage the capitalization patterns of tokens. In many cases, acronyms and abbreviations consist of uppercase letters. By identifying uppercase patterns within the tokens, we can detect potential acronyms/abbreviations and separate them from adjacent words by adding spaces.

Here's the code snippet that handles acronyms and abbreviations −

def add_spaces(text):
   nlp = spacy.load('en_core_web_sm')
   doc = nlp(text)
   words = []
   prev_token = None
   for token in doc:
      if not token.is_space:
         # Check for uppercase patterns to identify acronyms/abbreviations
         if prev_token and token.text.isupper() and not prev_token.text.isupper():
            words.append(' ')
         words.append(token.text)
      else:
         words.append(' ')
      prev_token = token
   return ''.join(words)

In the code above, within the add_spaces function, we iterate over the tokens in the processed document. We maintain a reference to the previous token using the prev_token variable. If the current token is not a space, we check if it is entirely in uppercase using token.text.isupper(). Additionally, we ensure that the previous token is not in uppercase to avoid incorrectly identifying consecutive uppercase words as acronyms.

If the conditions are met, we add a space before the token to separate it from the previous word, indicating that it might be an acronym or abbreviation. Otherwise, we append the token to the words list as usual.

Conclusion

We have explored the approach of adding spaces between potential words using the capabilities of Python programming. By harnessing the power of the spaCy library and its pre-trained language models, we can intelligently tokenize and separate text strings that lack appropriate spacing. This technique proves particularly valuable in various text preprocessing and data cleaning tasks. Remember to experiment with different datasets and customize the code according to your specific requirements.