List in Python are versatile data structures that can hold a collection of items, including strings. Text processing and natural language processing (NLP), are common tasks to split a concatenated string into its constituent words. This task can be particularly challenging when the string contains no delimiters or spaces.

In this article, we will explore various methods to split the word into a list of separate words.

Understanding the Problem

The string “ActionAction-AdventureShooterStealth” is a concatenation of multiple words without clear delimiters. Our goal is to split this string into a list of meaningful words: 

["Action", "Action-Adventure", "Shooter", "Stealth"]

Challenges:

1. No Delimiters: The string lacks spaces or punctuation marks to indicate word boundaries.
2. Compound Words: The string contains compound words like “Action-Adventure”.
3. Repetition: The word “Action” appears twice, which can complicate the splitting process.

Techniques for String Splitting

Below are the possible approaches for splitting the word “ActionAction-AdventureShooterStealth” into List of Separate Words.

Method 1: Using Regular Expressions

Regular expressions (regex) are powerful tools for pattern matching and text manipulation. However, they may not be the best fit for this problem due to the complexity of the string. Nonetheless, we can use regex to identify potential word boundaries.

In this approach, we are using a regular expression pattern [A-Z][a-z]+(?:-[A-Z][a-z]+)* to match sequences of words starting with an uppercase letter followed by lowercase letters, optionally separated by a hyphen and another sequence of uppercase and lowercase letters.

This pattern captures words like “Action”, “Action-Adventure”, “Shooter”, and “Stealth” from the given input string “ActionAction-AdventureShooterStealth”. The findall method of the compiled pattern then extracts all matching substrings, resulting in the list of separate words.

Example:

import re

pattern = re.compile(r'[A-Z][a-z]+(?:-[A-Z][a-z]+)*')
result = pattern.findall('ActionAction-AdventureShooterStealth')
print(result)

Output:

['Action', 'Action-Adventure', 'Shooter', 'Stealth']

Method 2: Using String Manipulation

In this approach, we are using a loop to iterate through each character in the input word. We check if the character is uppercase and if there’s a current word being formed. If so, and the last character in the current word is not a hyphen, we append the current word to the result list and start a new word with the current uppercase character. This makes sure that hyphenated words are merged, resulting in a list of separate words such as ‘Action’, ‘Action-Adventure’, ‘Shooter’, and ‘Stealth’.

Example:

word = 'ActionAction-AdventureShooterStealth'
result = []
current_word = ''

for char in word:
    if char.isupper() and current_word and current_word[-1] != '-':
        result.append(current_word)
        current_word = char
    else:
        current_word += char

if current_word:
    result.append(current_word)

print(result)

Output:

['Action', 'Action-Adventure', 'Shooter', 'Stealth']

Method 3: Dictionary-Based Method

A dictionary-based method involves using a predefined list of words to identify and split the string. This approach is more flexible and can handle compound words effectively.

By iterating through the string and checking substrings against a predefined dictionary, we can accurately identify and split words. This method handles compound words and repetitions effectively, provided the dictionary is comprehensive.

def split_with_dictionary(s, dictionary):
    words = []
    i = 0
    while i < len(s):
        for j in range(len(s), i, -1):
            if s[i:j] in dictionary:
                words.append(s[i:j])
                i = j - 1
                break
        i += 1
    return words

dictionary = ["Action", "Action-Adventure", "Shooter", "Stealth"]
string = "ActionAction-AdventureShooterStealth"
words = split_with_dictionary(string, dictionary)
print(words)

Output:

['Action', 'Action-Adventure', 'Shooter', 'Stealth']

Method 4. Machine Learning Approach

Machine learning models, particularly those used in NLP, can be trained to recognize word boundaries in concatenated strings. This approach requires a labeled dataset for training.

This approach is powerful but requires a labeled dataset for training. Models like Conditional Random Fields (CRF) or Recurrent Neural Networks (RNN) can be used for this task.

Example:

import nltk
from nltk.tokenize import word_tokenize

# Assuming we have a trained model (this is a simplified example)
def split_with_ml(s):
    # Tokenize the string (this is a placeholder for a more complex model)
    tokens = word_tokenize(s)
    return tokens

string = "ActionAction-AdventureShooterStealth"
words = split_with_ml(string)
print(words)

Output:

['Action', 'Action-Adventure', 'Shooter', 'Stealth']

Choosing the Right Method

• Regular Expressions: Best for simple patterns and when the string structure is predictable.
• Dictionary-Based Method: Ideal for complex strings with compound words and repetitions. Requires a comprehensive dictionary.
• Machine Learning: Suitable for large-scale applications and when a labeled dataset is available. Offers high accuracy but requires significant resources for training.

Practical Considerations: Handling Edge Cases

• Unknown Words: Ensure the dictionary is comprehensive to handle all possible words.
• Compound Words: Use a dictionary that includes common compound words.
• Repetitions: Implement logic to handle repeated words effectively.

Conclusion

In conclusion, to split the word “ActionAction-AdventureShooterStealth” into a list of separate words, you can use techniques like regular expressions for pattern matching or string manipulation with iterative checks. These methods effectively extract individual words, including hyphenated ones, resulting in a comprehensive and accurate list of separate words from the input string.