Enter two texts that you wish to compare. Click the "GET COMPARED TEXT" button to begin the process. Upon successful completion of the process, you will see a text comparison.
Text Similarities : Estimate the degree of similarity between two texts
We always need to compute the similarity in meaning between texts.
- Search engines need to model the relevance of a document to a query, beyond the overlap in words between the two. For instance, question-and-answer sites such as Quora or Stackoverflow need to determine whether a question has already been asked before.
- In legal matters, text similarity task allow to mitigate risks on a new contract, based on the assumption that if a new contract is similar to a existent one that has been proved to be resilient, the risk of this new contract being the cause of financial loss is minimised. Here is the principle of Case Law principle. Automatic linking of related documents ensures that identical situations are treated similarly in every case. Text similarity foster fairness and equality. Precedence retrieval of legal documents is an information retrieval task to retrieve prior case documents that are related to a given case document.
- In customer services, AI system should be able to understand semantically similar queries from users and provide a uniform response. The emphasis on semantic similarity aims to create a system that recognizes language and word patterns to craft responses that are similar to how a human conversation works. For example, if the user asks “What has happened to my delivery?” or “What is wrong with my shipping?”, the user will expect the same response.
What is text similarity?
Text similarity has to determine how ‘close’ two pieces of text are both in surface closeness [lexical similarity] and meaning [semantic similarity].
For instance, how similar are the phrases “the cat ate the mouse” with “the mouse ate the cat food” by just looking at the words?
- On the surface, if you consider only word level similarity, these two phrases appear very similar as 3 of the 4 unique words are an exact overlap. It typically does not take into account the actual meaning behind words or the entire phrase in context.
- Instead of doing a word for word comparison, we also need to pay attention to context in order to capture more of the semantics. To consider semantic similarity we need to focus on phrase/paragraph levels (or lexical chain level) where a piece of text is broken into a relevant group of related words prior to computing similarity. We know that while the words significantly overlap, these two phrases actually have different meaning.