Communication is no longer just a human prerogative. As new technologies develop, including artificial intelligence, content is being produced and disseminated automatically or by machines. This is a new dimension of communication, one of human-machine interaction, in which Natural Language Processing (NLP) plays an important role. Let’s try to understand what it is, how it works, how it performs and the challenges that need to be resolved.
By Natural Language Processing (NLP), we mean
a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data.
This hybrid discipline involves computer science, linguistics and artificial intelligence. As the name implies, it is the computer processing of human (or natural) language. Its purpose is, therefore, to allow a machine to decipher and understand written and spoken human language (without prejudice to all the implications related to the meaning of the natural language that we will see later).
The main problem of human-machine interaction is the multitude of communication languages. However, if we ask an NLP-based virtual assistant (e.g. Google Assistant, Siri or Alexa) a question, we generally get adequate and consistent results or answers. This is due to systems that allow a machine to grasp what is said or written in natural language, analyse it, extract the necessary information and process a meaningful response translated back into human language.
In this flow, we can identify two distinct moments: understanding the question and generating an answer. In both cases, two techniques closely related to NLP are employed: Natural Language Understanding (NLU) and Natural Language Generation (NLG). In the first, the expressed request is codified through understanding and interpreting the natural language; in the second, the data needed to process a response is decoded and transformed into natural language.
Focusing on the former, NLP analyses the syntactic and semantic aspects of the human language for a more adequate understanding. In this sense, the application of Parsing and Named Entity Recognition (NER) and Relation Extraction (RE) techniques make it possible to distinguish the form and structure of a text, extract the keywords (Information Extraction) and identify the relationship between them to grasp the meaning.
The earlier example helps us understand how versatile Natural Language Processing is and how frequently it is used. NLP can not only take many different forms but it can also be applied in many different professional sectors. From voice assistants on our smartphones to text readers, automatic translators and chatbots, all the way to individual artificial intelligence technologies developed to meet a company’s specific needs.
This is the case for Inda (INtelligent Data Analysis), Intervieweb’s proprietary artificial intelligence technology developed to optimise the search and selection process and natively integrate the results into the ATS In-recruiting. Amongst its numerous capabilities, the Natural Language Processing and Deep Learning algorithms help to accelerate the reading and analysis of CVs, automate the pre-screening of applications and simplify the semantic search of candidates directly within the software.
Natural Language Processing brings many challenges, mainly due to the complexities and ambiguities of languages. We only have to think about the different meanings that a word can assume, the tone of voice that can change its meaning and the other interpretation of a sentence depending on its context.
These factors are indicators that, for a correct understanding of natural language, we have to consider the role of the context or the surrounding environment. In this sense, the increasingly close link between neuroscience and the world of artificial intelligence – as already addressed in our previous article – will significantly contribute to smoothing one of the wrinkles that must be resolved.