Dig deeper or test it for yourself

Information is key, and the Peltarion team is continuously producing educational and inspirational material to help you along your AI journey. And below, we have listed some useful content to help you get started with your own Text similarity projects on the platform.

02/ Blog - A business view on semantic similarity

Natural Language Processing (NLP) is a field within AI that aims to understand the way humans communicate with each other and how to build systems capable of replicating that behavior. The latest advances in NLP capture semantics in a language in ways that were not possible before, opening a wide range of opportunities for companies to implement AI. In this blog, I provide concrete examples of how text similarity -a task within NLP- can improve efficiency in your business. All you need is text data.

03/ Blog - Search text by semantic similarity

Images and videos may take up a lot of space on the Internet but with 300 billion emails, half a billion tweets, and over 3 billion Google searches made each day, text is still a big player in digital life. Let's see how deep learning can help you navigate through endless lines of prose.

04/ Webinar - A closer look at text similarity

In this introductory webinar, we take a closer look at our newly announced product feature, Text similarity. With the launch of Text similarity, you will now be able to build models on the platform that can find and compare texts that are similar in context and meaning. This means that for a given set of texts, the model will give you a quantifiable measure of how alike they are as well as give you the best matches. All without the need to write a single line of code.

05/ Tutorial - Find similar google questions with text similarity

Text similarity is a way to quantify the similarity between two pieces of text, for instance, two questions written in natural language. Similarity search is faster than direct text string comparison, and allows some flexibility in the results. In this tutorial, you will solve a text similarity problem using a Google question dataset as input.

Multilingual book corpus

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Johan Hartikainen
Sales Lead
Peiman Momeni
Sales Lead