Natural Language Processing is at the core of Erudit’s AI technology, so our psychologists and AI experts want to clarify basic terms to help you understand what’s behind our software!

Natural Language Processing (NLP) is a concept discovered back in the 1970s, but it wasn’t until recently that it started evolving exponentially as larger volumes of data became available. In order to better understand how this brand new language processing model works, let us first clearly define what a language is.

What is a language?

A language is a set of infinite sentences that are formed by combinations of words. These combinations must be syntactically and semantically correct.

The major function of a language is to express thoughts, observations, and needs.It acts as a means of verbal communication between individuals. This function is carried out by vocal sound signals (voice) and/or by written signals (text).

We can distinguish between two types of languages: Natural Languages (English, German, Spanish, etc.) and Formal Languages (mathematical, logical, etc.).

1. Natural Language

Natural Language (NL) is the medium we use on a daily basis to communicate with each other. It has been shaped by human experience and can be used as a primary tool for analyzing highly complex situations and logical reasoning.

The semantic components of Natural Language generate the richness of its great expressive power and add value to its reasoning function. On the other hand, the syntax of Natural Language can be easily modeled by the second type of language, the Formal Language. 

Characteristics of Natural Language:

  • It was developed through progressive enrichment prior to any attempt of theory formation.
  • Its expressive character is derived largely from the richness of the semantic component.
  • It may never be completely formalized, with meanings changing and evolving.
  • All languages are systematic. They are governed by a set of interrelated systems.
  • They are conventional and arbitrary. They obey rules, such as assigning a particular word to a particular thing or a concept. 
  • They are redundant, meaning that the information in a sentence is signaled in more than one way.

2. Formal Language

Formal Language is a type of language developed in order to express situations that are specific to various branches of science. The words and sentences of a Formal Language are perfectly defined (a word maintains the same meaning irrespective of its context or use).

This type of language is not characterized by any semantic component outside of its operators and relations. It can be used to model theories of mechanics, physics, mathematics, electrical engineering, or any other discipline, without any ambiguity.

Characteristics of Formal Language:

  • It is attributed to a pre-established theory.
  • It is distinguished by having hardly any semantic components.
  • Presents the possibility of increasing the semantic component according to the theory under formalization.
  • The syntax produces unambiguous sentences.
  • Numbers play a crucial role. 
  • Its formalization is complete and, therefore, there is a huge potential for computational construction.

Within the Formal Language, we can find the Programming Language that is defined as a set of elements structured in accordance with grammatical rules that allow programmers to develop a program. This type of language has two important elements.

  • The syntax which is the established order of the lexical components. It is the set of rules that define the combination of symbols that form correctly structured elements or expressions.
  • The semantic which ensures that each sequence used has a correct meaning.

So what is Natural Language Processing (NLP)?

By bringing together the Programming Language and the Natural Language, we obtain the Natural Language Processing. It is a field of Artificial Intelligence that gives the machine the ability to interpret, understand and derive meaning from human language. The NLP is at the core of the following applications of AI and integrated machine learning:

  • Automatic translation
  • Information retrieval
  • Information extraction and summarization
  • Intelligent tutoring
  • Cooperative problem solving
  • Voice recognition

Erudit applies NLP to detect the true sentiment of the workforce through their communications data. We use semantic analysis for the interpretation and understanding of human language in large amounts of data. We then use Neural Networks (AI) to detect burnout risk, engagement, turnover risk, and other wellbeing metrics.

If you want to know more about Erudit AI’s technology and what it does, click here!

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