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.
A business view on semantic similarity
02/ What is semantic similarity?
03/ Where can I apply text similarity?
Semantic similarity can be applied in multiple scenarios where topics need to be found in different documents or text is compared or highlighted for specific purposes. Here are some use cases:
- In Human Resources or in a Job market system a description of a desired role can bring to the applicants several job positions that best fit their interests. The results are found in a semantic way that goes beyond keyword search opening opportunities for job seekers and human resources professionals to find the best candidate-job matches. After all, many job titles may be called in different ways from organization to organization.
- Smart search implementation on user manuals, help documents or product catalogs where the topics are found independent of typos, the language that is used or the writing style of the person formulating the query sentence.
04/ Why is NLP and text similarity suddenly so popular?
Traditional NLP models often treated words the same regardless of the context or word order making them linguistically more “naive”. The rise of transformers-based NLP models revolutionized the way systems can interpret and understand language, opening a wide range of opportunities for companies to implement AI.
Transfer learning also plays an important role as it enables the knowledge acquired by these models -as the underlying language structure- be used to solve other, related problems effortlessly and with a much smaller amount of data.
In the Peltarion platform, we have NLP models like English BERT, Multilingual BERT, Universal Sentence Encoder (USE) and XLM-R (model: XLM-R ← SBERT-nli-stsb) the two latter optimized for semantic similarity tasks.
If you are interested in a more technical description of text similarity, here is a great blog by my colleague Romain Futrzynski Search text by Semantic similarity.
05/ Get started!
Finally, it is time to roll up your sleeves and building some powerful AI solutions with NLP and text similarity on the no-code Peltarion platform. Here is a tutorial where you can create a text similarity model that finds similar Google questions that others have asked. For some additional inspiration, check out our tutorial catalog.
We hosted an introductory webinar for text similarity on April 8th, 2021. Watch the recording here: A closer look at Text similarity.
If you want to explore more in detail how text can be used for your next AI project or need some guidance, please get in touch with me at email@example.com. We’d love to hear from you!