Probably many of us have heard about natural language processing. And today we will learn more about this.
To begin with, many people want to create their own unique project with this technology. Finding good specialists is not easy today. However, we know the solution! Just contact the professionals of Unicsoft – artificial intelligence development company and get your project realized.
PLN is a discipline with over 50 years of research and development.
What is natural language processing?
Natural language processing is a field of artificial intelligence that deals with the study of how machines communicate with humans using natural languages such as Spanish, English, or Chinese.
What is natural language processing used for?
Natural language processing is currently used in different fields and for different functions, such as:
Natural Language Comprehension
Natural Language Understanding is the part of natural language processing that is responsible for interpreting a message and understanding its meaning and intent, as a human would do. The system requires datasets in a specific language, grammar rules, semantic and pragmatic theory (to understand context and intentionality), etc.
Natural language generation
Natural language generation gives a machine the ability to autonomously create a new message in human language. Thus, these models do the following: select information to be reproduced (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 must be trained to work properly.
Search for information
Information retrieval is a field of computer science that is responsible for processing the text of documents in order to be able to extract specific parts based on keywords. For example, methods such as extracting structured information (allows you to get a piece of text from a document that contains what you are looking for) or systems for answering user questions (which return an answer from a set of answers to an already existing query associated with query keywords). It doesn’t generate new phrases, so you don’t need to use grammar rules. He’s not as smart as the natural language generation.
Speech recognition and synthesis
Voice recognition systems process messages with a human voice, transform them into text, interpret and understand their intentionality, and after generating a response in the form of text, it is converted back to a human voice through voice synthesis. Speech or voice synthesis is what enables a machine to generate and reproduce speech in natural language.
Automatic translation
Machine translation or machine translation in English is an area of research in computational linguistics that studies 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, and its translator uses its own statistical engine. Text-based autocorrect and autocomplete systems also use natural language processing.
Summary and classification of texts
Natural language processing is also used to automatically summarize long texts or extract keywords to rank them. In many cases, due to the large volume of documentation or its length, the use of these systems helps in sectors such as the legal sector to find parts within laws or to summarize a large amount of documentation.
Another use for this classification function is spam detection. Companies like Google use this technology to classify the text of emails and determine if they are spam or not. To do this, they take keywords such as “free” or “discount,” capitalized terms, or exclamation marks.
Detecting feelings or emotions
One of the newer uses of PLN is sentiment analysis. More and more companies and marketers are using this technology to find out how users feel about a brand, product or service using input such as posts, comments or reactions on various social networks.
Depending on the purpose of the application, morphological, syntactic, semantic or pragmatic analysis will be applied. For example, a text-to-speech converter does not need semantic or pragmatic analysis. But the conversational system requires very detailed information about the context and the subject area.
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