Transform text into semantic fingerprints for pinpoint accuracy with minimal computing resources
Semantic fingerprints capture the different meanings of words based on thousands of parameters and form clusters of similar contexts. For document processing tasks like classification and semantic search, the system just needs to measure semantic overlaps between semantic fingerprints - a highly efficient computational approach that powers the processing of very large amounts of text with less computing resources.
Convert the input text into a semantic fingerprint. First, each word is converted into its fingerprint representation. Then these word representations are aggregated and sparsified to create the text fingerprint.
Instead of training the classifier with many labeled examples, one reference fingerprint can be used to describe a class. This can be used for real-time classification of emails or social media posts, or for screening candidate profiles for example.
With semantic fingerprints, queries in natural language can be directly compared with the indexed documents, improving both recall and precision. This can boost intranet search.
With semantic fingerprints, language models can be trained with comparatively little training data and computing resources while achieving similar accuracy levels as with transformers.
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Semantic fingerprints encapsulate all meanings associated with a text in a topographical representation where similar meanings are placed close to each other.
Semantic fingerprints allow direct comparison of the meanings of any two pieces of text, showing thousands of semantic relations.
If two semantic fingerprints look similar, it means that the texts are semantically similar too.
