You have surely heard the term “Natural Language Processing” (PLN) or its name in English Natural Language Processing (NLP) more than once. This technology is not new, but it is true that its evolution in recent years has undergone exponential growth due to the large volumes of available data, current computing capacity and advances in the field of algorithms. Do you know what Natural Language Processing is and what is it currently used for? We will tell you about it in this article.
- 1 What is Natural Language Processing (PLN or NLP)
- 2 What is Natural Language Processing (PLN or NLP) used for?
What is Natural Language Processing (PLN or NLP)
Natural language processing (PLN or NLP) is a field within artificial intelligence and applied linguistics that studies interactions through the use of natural language between humans and machines. More specifically, it focuses on the processing of human communications, dividing them into parts, and identifying the most relevant elements of the message. With the Comprehension and Generation of Natural Language, it seeks that machines can understand, interpret and manipulate human language.
Virtual assistants or chatbots are one of the most popular utilities of the PLN, but they are not the only one. In addition, it is important to understand that NLP does not give a chatbot intelligence, it only gives it the ability to process and generate human language. If you want to provide intelligence to a virtual assistant, you would have to use systems such as rules or neural networks.
Many times when talking about natural language processing, some people only relate it to chatbots, so we are going to see other uses of the PLN.
What is Natural Language Processing (PLN or NLP) used for?
Natural language processing (PLN or NLP) is currently used in different areas and for different functions, such as:
Natural Language Comprehension (CLN or NLU)
Natural language understanding (CLN or NLU) is the part of natural language processing that is responsible for interpreting a message and understanding its meaning and intention, just as a person would. For the system to work you need datasets in the specific language, grammar rules, semantic and pragmatic theory (to understand the context and intentionality), etc.
Natural language generation (GLN or NLG)
The generation of natural language (GLN or NLG) gives the machine the ability to create a new message in human language autonomously. In summary, what these models do is: choose the information to reproduce (depending on the interpretation of the message to be answered), decide how to organize it and how to reproduce it (vocabulary and grammatical resources, morphology, syntactic structures, etc.). These models generate new phrases word for word and have to be trained to work correctly.
Information retrieval (RI or IR)
Information Retrieval (RI) or in English Information Retrieval (IR), is the field within computer science that is responsible for processing document texts, to be able to retrieve specific parts based on keywords. For example techniques such as the extraction of structured information (allows you to obtain from a document the piece of text in which what you are looking for is) or response systems to user questions (which returns a response from a battery of responses to a query already existing, associated with keywords of the query). It does not generate new phrases, so you do not need to use grammar rules. It is not as “smart” as the Natural Language Generation.
Speech recognition and synthesis
Voice recognition systems process messages in human voice, transform them into text, interpret them and understand their intentionality, and after generating the response in text, it is transformed back into human voice through synthesis voice. The synthesis of speech or voice is what enables the machine to be able to generate and reproduce speech in natural language.
Machine Translation in English is a field of research within computational linguistics that studies the systems capable of translating messages between different languages or languages. For example, Google is one of the companies that has invested the most in machine translation systems, with its translator using its own statistical engine. Autocorrect and autocomplete text systems also use Natural Language Processing (PLN or NLP).
Summary and classification of texts
Natural language processing is also being used to automatically summarize long-length texts or extract keywords to rank them. Many times, due to the large amount of documentation or the length of it, using these systems helps in sectors such as legal to find parts within the laws, or summarize a large amount of documentation.
Another use that is given to this classification function is to detect spam. Companies like Google use this technology to classify the texts of emails and detect whether they are spam or not. For this, they take keywords like “free” or “discount”, the condition of capitalized words or the exclamations.
Detection of feelings or emotions
One of the newer uses of the PLN is sentiment analysis. More and more companies and marketing professionals are using this technology to find out how users feel about a brand, product or service, using input data such as messages, comments or reactions on different social networks.