Tokenization of Unspaced Text into Words using Efficient Algorithms

In the realm of natural language processing, the ability to split a continuous stream of characters into meaningful words is crucial. This process, known as tokenization, is particularly challenging when dealing with text that lacks spaces or delimiters.

Challenge Statement

The task at hand involves splitting an input string like "tableapplechairtablecupboard..." into a list of words, taking into account the possibility of ambiguous substrings where a sequence can form multiple words (e.g., "cupboard" can be "cup" or "board").

Algorithm: Exploiting Word Frequency

A naive approach of iteratively identifying the longest possible word at each position yields unsatisfactory results in real-world scenarios. To overcome this limitation, we leverage an algorithm that incorporates word frequency distribution.

Modeling Word Frequency

We assume that word frequencies follow Zipf's law, which states that the probability of encountering the n-th frequent word is approximately 1/(n * log(N)), where N is the total number of words in the language. Using a precomputed cost dictionary that encodes this relationship, we can assign a cost to each potential word candidate.

Dynamic Programming Approach

To determine the optimal word segmentation, we employ dynamic programming. We iterate through the input string, maintaining a running cost value for each potential split point. At each position, we evaluate the candidate words starting from the end of the string and select the split with the lowest cost.

Algorithm Implementation

The provided Python code offers a concise implementation of this algorithm:

from math import log

# Precomputed word cost dictionary using Zipf's law
wordcost = ...

# Helper function to find the best word match based on cost
def best_match(i):
    ...

# Function to infer spaces in the input string using dynamic programming
def infer_spaces(s):
    ...

Example Usage

To utilize this code, simply input the continuous text string as follows:

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

Results and Evaluation

This algorithm demonstrates exceptional performance even with a limited word dictionary. It successfully tokenizes complex text with high accuracy.