Struggling with AI or full-stack development? Our experts are here to guide you: tailored advice, technical integration, and more. Reach out at [email protected].

Semantic Similarity API

What is Semantic Similarity?

Semantic similarity is about detecting whether 2 pieces of text have the same meaning or not.

For example, you might want to know whether the 2 following blocks of text are talking about the same thing:

Batch inference is very powerful because it will take almost the same time for your model to address several requests as it takes to address 1 request. Under the hood some operations will be factorized, so that instead of doing everything n times, the model only has to do it once.
Batch inference is a good way for your model to address more requests faster. Some operations are actually factorized in order to do things only once.

They clearly DO talk about the same thing and pretty much have the same meaning.

Sending these 2 blocks of text to a semantic similarity model would return a score like 0.90, meaning that, according to the model, the 2 inputs have the same meaning. On the other hand, a low score would indicate that the inputs don't have the same meaning.

Noun Chunks

Why Use Semantic Similarity?

The quality of semantic similarity has recently dramatically improved and has led to many interesting applications. Here are some examples:

Plagiarism Checking

Thanks to semantic similarity, you can automatically detect whether a piece of text is a paraphrase of another piece of text.

Semantic Search

Modern search engines must be able to detect the intent behind a search request and then match that intent against a high volume of text samples. This is a great application for semantic similarity.

Opinions Analysis

Thanks to semantic similarity, it is possible analyze a huge volume of Tweets, conversations, comments... and then detect some trends out of them.

Recommendation Systems

In the domain of content recommendation (e.g., news, articles, products, or movies), semantic similarity can be used to recommend items that are semantically related to those a user has previously liked, viewed, or purchased. By analyzing the semantic content of items, systems can identify and suggest other items with similar themes or topics, enhancing personalization and user engagement.

NLP Cloud's Semantic Similarity API

NLP Cloud proposes a semantic similarity API that allows you to perform semantic similarity out of the box, based on Sentence Transformers models like Paraphrase Multilingual Mpnet Base v2 and more.
The response time (latency) is low for these models.

For more details, see our documentation about semantic similarity here.

Testing semantic similarity locally is one thing, but using it reliably in production is another thing. With NLP Cloud you can just do both!

Try semantic similarity
for free

Frequently Asked Questions

What is semantic similarity?

Semantic similarity is a measure of the degree to which two pieces of text (such as words, phrases, or documents) are related in meaning or context. It is often used in natural language processing and information retrieval to determine how similar two pieces of text are in terms of their semantic contents.

How is semantic similarity measured?

Semantic similarity is measured using various computational models and algorithms that analyze the meaning of words, phrases, or sentences and quantify the degree to which they are related in meaning. Techniques include cosine similarity on word embeddings, such as those generated by Word2Vec or BERT models, as well as more complex models that take into account contextual nuances or hierarchical relationships within ontologies.

What is the difference between semantic similarity and semantic search?

Semantic similarity and semantic search usually use the same techniques under the hood, but semantic similarity compares 2 pieces of text while semantic search compares 1 piece of text to many documents.

What is the difference between semantic similarity and semantic relatedness?

Semantic similarity measures the degree to which two words or phrases are synonymous, focusing on their likeness in terms of meaning within the same context. In contrast, semantic relatedness encompasses any type of semantic relationship between concepts, including antonymy, membership, part-whole relations, etc., thus covering a broader range of connections beyond mere similarity.

What tools and resources are available for researchers working on semantic similarity?

Researchers working on semantic similarity have access to various natural language processing tools and libraries such as Word2Vec, GloVe, and BERT for embedding generation, along with datasets like WordSim-353, SentEval, and SimLex-999 for evaluation. Additionally, platforms like TensorFlow and PyTorch provide comprehensive environments for implementing and experimenting with neural network models related to semantic similarity tasks.

How to evaluate the accuracy of semantic similarity?

To evaluate the accuracy of semantic similarity, one typically employs benchmark datasets containing pairs of texts annotated with human-judged similarity scores, and then compares these to the scores generated by the semantic similarity model using metrics such as Pearson correlation, Spearman's rank correlation, or Mean Squared Error (MSE). The closer the model's scores are to the human-judged scores, the more accurate the model is considered to be.

What languages does your AI API support for semantic similarity?

We support semantic similarity in 50 languages: Albanian, Arabic, Armenian, Bulgarian, Burmese, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, French (Canada), Galician, German, Georgian, Greek, Gujarati, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Kurdish, Latvian, Lithuanian, Macedonian, Malay, Marathi, Mongolian, Norwegian Bokmål, Persian, Polish, Portuguese, Portuguese (Brazil), Romanian, Russian, Slovak, Slovenian, Serbian, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu, Vietnamese

Can I try your semantic similarity API for free?

Yes, like all the models on NLP Cloud, the semantic similarity API endpoint can be tested for free

How does your AI API handle data privacy and security during the semantic similarity process?

NLP Cloud is focused on data privacy by design: we do not log or store the content of the requests you make on our API. NLP Cloud is both HIPAA and GDPR compliant.
Try semantic similarity
for free