Fact Checking
Text Comparison: Analyzing Similarities and Differences
Uncover the nuances of text comparison online. From plagiarism checks to academic research, explore its applications, workings, and challenges in various fields.
Text comparison online involves analyzing two or more texts to identify their similarities and differences. It’s a major component of plagiarism checkers as well as academic research, legal studies, literary analysis and even content management. But how exactly does it work, when do you use it and what are some of the challenges when comparing two pieces of text? Let’s take a closer look:
Text comparison involves juxtaposing two (or more) types of documents to see where they overlap and where they diverge from each other. It can be a s simple as looking for exact matching strings of text or as advanced as understanding differences in semantics. Having a text similarity checker is a fundamental tool to have across a number of fields beyond plagiarism detection or intellectual property protection.
The right text similarity checker is vital in comparing text for stylistic analysis, authorship attribution or linguistic analysis. It’s also useful for historical research, translation and content management. But how exactly do you check similarities between two texts?
There are several ways to check for differences in two texts, including:
This is the simplest way to compare two texts and involves looking for identical strings of text in different documents. This method is often used in basic plagiarism detection before moving on to more advanced types of text comparison.
Semantic analysis is more involved and involves understanding the meaning of words and sentences as they appear in texts. Understandably the tools used for this type of deep analysis are more sophisticated and can detect more advanced types of plagiarism or paraphrasing in which the same (or similar) ideas are expressed differently.
This method looks at how sentences are structured in order to check similarities between two texts. It concentrates on analyzing how the same idea is expressed in different ways.
Stylometry focuses on word choice, sentence length and features that mesh with the “style” of writing. It’s often used to determine and attribute authorship based on one’s particular type of writing.
Technology, particularly the introduction of artificial intelligence, has revolutionized our ability to compare text documents and check texts for similarities or differences. With the latest advances in AI developments, we’re now able to do deeper, more accurate and more efficient text checks. Here are some of the many developments that are currently being used and refined to help with text comparison.
Known as NLP, Natural Language Processing is a branch of AI development that focuses on how computers and human language interact. NLP algorithms are designed to understand and interpret human language which allows for a deeper analysis of text matching than what simple string matching offers.
For example, by using semantic analysis, NLP models can understand the meaning behind words and sentences which lets them compare text based on their meeting rather than the lexical similarity of words used.
Context understanding also allows NLP algorithms to analyze the surrounding context in which the words are used. This allows them to sidestep common language pitfalls such as instances where the same word can appear in text and have different meanings based on how it’s used.
For example, if an NLP were to encounter the word “bank”, without the right context, it wouldn’t know if the author was referring to the financial institution or the land that slopes down to a river. Advanced models like GPT and BERT look at the entire sentence and surrounding sentences to understand the context. In some cases, NLP algorithms can also look at syntactic structure. If words like “river” or “deposited '' appear, it gives them clues on what meaning of “bank” the author is referring to.
Machine learning is a subcategory within artificial intelligence that involves the training of algorithms and teaching them to make decisions based on the data they’re presented with. When it comes to acting as a text similarity checker, machine learning (ML) algorithms can spot patterns and anomalies in text which makes them excellent tools in the fight against plagiarism, since unusual patterns can be a hallmark of copied text.
What’s more, ML models can adapt and improve over time as they are fed more data. This allows text comparison to become more accurate and more efficient overall.
If machine learning is a sub-branch of AI, then deep learning is considered a sub-branch of machine learning. With deep learning, neural networks are used to analyze data. For example, deep learning models, like Transformers, can handle complex tasks like sentiment analysis, syntactic parsing and much more, enabling for more nuanced and intelligent text comparison.
With deep learning, large language models like GPT-4, BERT and many more can understand and generate human-like text based on the huge datasets they’ve been fed. This allows them to handle more sophisticated text comparison tasks that go well beyond the basics.
AI-driven tools are quickly becoming indispensable in a wide range of industries. For example, in the legal field, AI text comparison tools can highlight differences in legal documents and analyze contracts while making suggestions that can save time and reduce the possibility of errors.
In the world of software development, AI can compare different versions of code, looking beyond syntax changes to understand how changes in logic can affect the entire program. This makes it invaluable as part of an overall version control system.
Even Originality.ai’s own plagiarism detection tool and text comparison tool leverages AI and large datasets to help spot potential plagiarism when comparing two versions of text or code to spot differences or similarities.
But even these rapid developments in the use of AI and technology as a whole come with their own set of challenges. Machine learning modules require training on vast amounts of data and considerable computing power.
There’s also the looming specter of bias in the training data itself, which can have a ripple effect on the output of the model. New developments in the field are aimed at greater energy efficiency and precision when it comes to understanding nuance, subtlety and emotion in writing.
Technology and AI as a whole has made a profound impact on the world of text comparison tools. The simple string matching programs of yesteryear don’t hold a candle to today’s more robust, efficient and complex machines. With AI, machine learning, deep learning and other technological developments now allowing computers to understand language, context and the meaning behind the word, the end result is more accurate and insightful analyses in a variety of industries, which are only looking to improve exponentially.
Try Originality.ai’s text similarity checker to review code or use it as an article similarity checker if you’re concerned about plagiarism or copying and put the latest text comparison technology to work for you!