That was the quick answer! If you're curious about how text comparison tools work in depth, we've written an article just for you. Enjoy reading!😊
As you know, you can compare two pieces of writing just by looking at them. If you read both texts carefully, you can find words, sentences, or ideas that are the same. This is how we naturally spot similarities. Text comparison software works in a similar way—but much faster and more accurately. Instead of reading word by word like a human, the software uses algorithms to scan and analyze the text automatically. It can quickly compare two papers, highlight matching parts, and even check if content has been copied from another source.
One of the simplest methods used in side-by-side text comparison follows these steps:
Now, let's go through each of these steps in detail with examples to see how they work!
The text is broken down into smaller parts, called tokens, which are usually words or phrases. This makes it easier to analyze.
Example
After tokenization:
The software compares the tokens from both texts to find similarities. It may look for exact matches (identical words) and partial matches (words with similar meanings).
Example
Example of matched tokens:
Once matches are found, the software generates a detailed similarity report that highlights matching words, phrases, or paragraphs. These highlighted sections make it easy to see where the texts overlap.
Users can view the report on-screen to analyze the results or download it as a file for further review.
This basic method is often used in plagiarism checkers, writing analysis, and even language learning tools. However, more advanced text comparison techniques go beyond just looking at exact word matches.
After breaking a text into smaller parts (tokens), the next step in text comparison is text matching, where the software finds similarities between two pieces of writing. Different algorithms are used for this, ranging from basic word-to-word matching to more advanced techniques that can detect paraphrasing and rewording. Here are some key text matching methods:
Now, let's take a closer look at each of these algorithms with examples to see how they work in action!
This is the simplest method, where the software looks for identical words, phrases, or sentences in both texts. If a sentence appears word-for-word in both documents, it is considered a match.
Example
Since the sentence is identical, this is an exact match.
Greedy String Tiling (GST) is an algorithm that finds longest matching sequences of words between two texts. It helps detect copied content even if parts of the text have been rearranged.
Example
GST detects that most of the words are in both sentences, even though the order is slightly different.
N-gram matching is a method where text is split into small sequences of words (n-grams), and these sequences are compared to find similarities.
Example
If the algorithm uses 3-word n-grams, it might detect: "a beautiful forest" (Text 1) ↔ "a beautiful forest" (Text 2)
N-gram matching is useful in plagiarism detection because it can find copied phrases even if some words are changed.
This algorithm is commonly used to compare sequences of text and find localized matches. Unlike exact matching, it allows for gaps and small changes in the text. It is useful for short text comparisons or detecting reworded phrases.
Levenshtein Distance measures how many changes (insertions, deletions, or substitutions) are needed to turn one text into another. A lower distance means the texts are more similar.
Example
Since only one letter is different, the Levenshtein Distance is 1.
Instead of only detecting exact matches, some algorithms use thesaurus-based databases or AI models to recognize words with similar meanings.
Example
Even though "trip" and "vacation" are different words, they have similar meanings.
