Natural Language Processing: what is it and how can you put it to use?

2018/ 27/02

Natural Language Processing has an enormous role when it comes to either chatbots or translation softwares. In today’s article you can get more information on this technology.

Natural Language Processing (or NLP) is a type of artificial intelligence that helps computers understand, interpret, and process natural human languages. NLP is technically a connecting link in order to make communication between computers and humans easier.

Without NLP, computers would still be able to understand the meaning of individual words; and with artificial intelligence, they could even answer questions. However, NLP helps the computer understand and process words and phrases in context. This means that when communicating with a machine, you don’t need to “translate” a given text into a form that is understood by the machine – you can talk to it in your own words (using natural language).

How does it work?

Natural Language Processing is made up of different elements and stages. NLP experts use methods and tools from a number of disciplines in order to make communication between humans and computers easier and smoother. Among these disciplines you find for example computational linguistics, artificial intelligence and information technology too.

The aim of NLP is to break down natural languages to smaller components, and to determine the relationship between these components. The more data (text) you have, the easier it is to find the correlations between the data – this means that it is also easier to get the complete picture of a language this way.

How can you apply NLP?

There are many ways you can use Natural Language Processing in everyday life and in business. You would have no idea in how many fields of study you can put a use to this branch of computational linguistics: you can use it for example to develop chatbots or digital assistants; to make everyday communication easier, or even translation.

Corpus linguistics

Corpus linguistics is a branch of applied linguistics whose point is that you can make assumptions and generalisations from a large body of (written or oral text-based) data. The enormous amount of text is analysed by using Artificial Intelligence and processed by NLP methods in order to be able to find correlations within or even between the given texts.

Texts can be analysed from a number of aspects:

  • Categorising content
    This means that the major keywords of the text are identified – and based on those, the different texts can be grouped.
  • Writing a summary of the text
    The AI can make a short summary from a text of a bigger volume.
  • Sentimental analysis
    NLP can even identify what kind of mood the author or speaker was in when producing the text, or what their general opinion of the world is. This can be determined from vocabulary and grammatical structures used in the text.
  • Syntactic analysis
    This type of analysis can play a major role in the case of texts whose date of origin is unknown. By analysing syntactic structures and words of the text together can help determine when and where the text was produced.

Natural Language Interface

The aim of Natural Language Interfaces (NLI) is to make database queries simpler. If you talk to them in a natural language, it is able to transform that into a query, to which the database responds by finding the appropriate data. These data get back to you in a natural language, too.

Machine translation

The first thing that comes to one’s mind when hearing about computational linguistics is machine translation and automated translation. The challenge of machine translation not only lies within the fact that the machine must be able to understand the text given to it in the source language, but it must also be able to translate the text into the target language so that the text is understandable for the speakers of the target language, too. Literary translations are even more complicated: you do not only have to transfer the contents of the text in the target language, but you also have to keep the atmosphere and the effect of the text on people. Machine translation is most probably quite far from that yet.

In the field of machine translation, you can also rely on Big Data besides Artificial Intelligence. Scientists whose expertise is translation and computational linguistics have found out that sometimes it is not logic that determines whether a given term is appropriate in a context but the number of occurrences. In these cases they decide whether a term is appropriate by looking at and comparing some already translated texts, and they examine which translation proved to be the right one.

Chatbots and digital assistants

Digital assistants like Google Assistant or Amazon Echo are without a doubt one of the most widespread forms of Artificial Intelligence. What’s more, businesses are starting to realise they need voice based technology in order to provide the best possible service and user experience to their clients.

The best AI on the market so far: we tried the Amazon Echo

In order for Alexa to be able to answer all your questions, you also need NLP. Until the time comes when artificial intelligence teaches itself or learns from other bots, the only source you can rely on when making a chatbot is natural languages.

This means that developers, AI experts and linguists all need to work together so that the chatbot eventually be able to understand what the user wants to say to them (or even ask them), to transform it into a form which can be processed by its own software, and to give a response to the user using natural language.

Sources: Wired, SAS, Expert System

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