SentiSquare’s NLP technology is based on distributional semantics. This approach enables us to represent the meaning of a text without any supervision. The principle goes as follows: “You shall know a word by the company it keeps” (Firth, 1957).
Essentially, words are presumed to have similar meanings if they occur in similar contexts. That opens an opportunity for the quantification of meaning: textual expressions can be represented as vectors in a high-dimensional semantic space, encoding the distribution of words over contexts (this is how we got the term "distributional semantics").
State-of-the-art semi-supervised machine learning techniques allow the training of custom NLP models directly on company-specific data, reaching human-like accuracy at highly optimized computational costs. Language-independent contextual patterns also make our technology suitable also for underserved languages.
The secret ingredient of the SentiSquare No-Code NLP platform is distributional semantics. The main idea of distributional semantics is that "meaning follows from context". We will extract the right meanings from your texts. In minutes, your data will be sorted, categorised, and you'll know what they're holding.
Built-in functionality for adding business rules to handle exceptions and combining models into a complex system allows companies to have text-driven processes under control during their daily operations.
Read about real-life experiences with the SentiSquare No-Code NLP solution.
