How Can We Split Text Without Spaces Into a List of Words?

Splitting Text Without Spaces into a List of Words

Introduction

This article delves into the complexities of efficiently splitting a text string devoid of spaces into a meaningful list of words. We explore an algorithm that leverages word frequency to achieve accurate results for real-world data.

The Algorithm

The algorithm operates under the assumption that words are independently distributed, following Zipf's law. This implies that the probability of encountering a word with rank 'n' in a dictionary is approximately 1/(n log N), where N represents the total number of words in the dictionary.

To infer the position of spaces, we employ dynamic programming. We define a cost function that utilizes the logarithm of the inverse of word probability. The optimal sentence maximizes the product of individual word costs, which can be efficiently computed using dynamic programming.

Implementation

The following Python code implements the algorithm:

<code class="python">import math

words = open("words-by-frequency.txt").read().split()
wordcost = dict((k, log((i+1)*log(len(words)))) for i,k in enumerate(words))
maxword = max(len(x) for x in words)

def infer_spaces(s):
    cost = [0]
    for i in range(1,len(s)+1):
        c,k = best_match(i)
        cost.append(c)

    out = []
    i = len(s)
    while i>0:
        c,k = best_match(i)
        out.append(s[i-k:i])
        i -= k

    return " ".join(reversed(out))</code>

Demonstration

Using the provided code, we can split a text string without spaces and obtain meaningful words:

s = 'thumbgreenappleactiveassignmentweeklymetaphor'
print(infer_spaces(s))

Results

The algorithm effectively infers the location of spaces, resulting in accurate word recognition for both short and long text strings. Even in the absence of explicit delimiters, the output maintains a high level of coherence and readability.

Benefits

The algorithm offers several benefits:

• Accurate word recognition, even in the absence of spaces
• Efficient time and memory consumption
• Ease of implementation and scalability for large text datasets

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