In recent years, we have heard a lot about natural language processing (NLP). Even today it has a greater echo in the digital world in the context of the Data Science. However, many people are not entirely clear about what this concept is.
The fact is that NLP is a fundamental tool for a large number of projects related to the data analysis and manipulationIts aim is to understand and analyse human languages in order to extract useful information. To try to resolve any doubts about this concept, in this article we will tell you about its main functionalities and operations.
In simple terms, natural language processing is a field of knowledge corresponding to the Artificial Intelligence (AI). In simplified form, its purpose is to deal with research and the way in which it is carried out. machines communicate with humanss thanks to the use of languages, as is the case, for example, when we use Spanish or English.
Let us consider that any kind of human language is susceptible to being processed by computer equipment. Of course, economic constraints will mean that only the most widely spoken languages in the digital field are likely to have applications that can be used.
Just think that, for example, Siri 'speaks' 20 languages. Google Assistant speaks another eight languages. Google Translate, on the other hand, takes the prize for being the one with the highest number of languages. More than a hundred!
NLP, in short, deals with the communication between machine and humans, so that the former has a intelligent behaviour and can understand the way we speak.
Thus, an NLP system - once it is programmed to be able to understand human language - will be able to provide information on a given topic, remember data, detect keywords and perform simple learning tasks.
The languages of human beings are expressed in writing by text, by voice and by the use of signs. Of these areas, NLP has made the clearest progress in the text processingThe data will be much more extensive and will be easier to obtain in electronic format.
In the case of audios, even if they are in digital format, they need to be processed to be transcribed into letters or characters. From there, it is possible to help a machine understand what was meant.
The way to deal computationally with a lenga will involve a mathematical process and the use of models. This is what is needed for the machine to be able to understand our languages and therefore, from there, to be able to act.
The models are usually based on three main areasMorphological analysis, syntactic analysis and semantic analysis.
The former are concerned with the way in which language is written. The second ones deal with how these words are combined together to form sentences. Finally, the third ones deal with how meaning is understood, i.e. what is most important for effective natural language processing.
The development of models of natural language processing is one of the main tasks of the data analysts and data scientists. These professionals prepare the models so that the engineers can implement them in a code that is truly efficient and functional.
In this sense, there are two kinds of general approaches to the problem of linguistic modelling, which are discussed below.
Linguists who take this approach write a number of rules for pattern recognition at the structural level, so they use concrete grammatical formalisms.
These rules, combined with all the data that is stored in the computer dictionariesThe aim is to define the patterns that must be recognised for the resolution of the task, both when searching for information and when translating a text, etc.
In this case, we can speak of the inverse approach. This type of model focuses on the use of data for trying to find patternswithout the need to define linguistic rules.
Algorithms are trained on large amounts of data and learn from them how human language works.
In this way, the more information we have previously provided, the better the efficiency of the natural language processing system we create. This allows us to have applications that can be used by people who speak different languages and dialects, and can be easily adapted to them.
Now we are going to look at some of the main ones when it comes to dealing with natural language processing. Not all of them can be applied in any NLP work, but the use of some or others will depend on the objective of the application itself.
It is based on the internal analysis of words that help to form sentences in such a way that lemmas, compound lexical units or inflectional features can be extracted. They are essential for basic information.
This is the analysis of sentence structure on the basis of the grammatical model used, both in the logical and statistical fields.
It helps with the interpretation of sentences, as well as eliminating ambiguities at the morphosyntactic level.
It adds the analysis of the context in which the interpretation. This includes the treatment of so-called figurative language, such as metaphor or irony, as well as the concrete knowledge necessary to understand a text.
Morphological analysis, as well as semantic and syntactic analysis, has to be applied due to the purpose of the application. In this case, we can think of text-to-speech converters as not requiring semantic or pragmatic analysis. With the conversational system, very detailed information is needed.
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This field of technology is increasingly gaining momentum, as it is necessary to provide insight into data. If you want to train in this field and become a high-demand professional, don't hesitate to sign up for Immune Institute's Data Science Bootcamp!
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