What Is NLP


Natural language processing (NLP) is a sphere of artificial intelligence that allows computers to understand, interpret and translate people’s languages. NLP connects different branches of knowledge, including computer science and linguistics. Such high-tech helps solve the problem of human-computer interaction.

A little background of NLP

When we try to analyze what is NLP, it is essential to understand that natural language processing appeared a long time ago. 

The first mention of NLP appeared in the 1950s when Alan Turing ran a test and determined that a computer is an intelligent machine. In this period, developers used rule-based methods for building NLP systems: word or sentence analysis and machine translation. Only experts, who knew the algorithms to build the rules, could construct such systems. 

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In the 1990s, the rapid development of the Internet made a lot of knowledge accessible. The progress sanctioned statistical learning methods to work on NLP tasks; they ensured magnificent strides to improve NLP. Statistical learning methods work with a specific database and describe its features.

In 2012 computer engineers began to use Deep-Learning instead of statistical learning; thanks to this, science entered a new round of development. Deep-Learning successfully processes raw data and researches its attributes.

Nowadays, a new approach to NLP is based on neural networks (neural NLP). A system facilitates machine translation, the operation of chatbots, and other procedures. 

The principle of natural language processing work

There are two principal stages of natural language processing: data preprocessing and algorithm development.

Data preprocessing provides the preparation and cleaning of textual information; thus, the computer is easier to analyze. Such preprocessing transforms materials into a workable form and highlights elements that ensure the algorithm’s operation. There are several preparation methods. First one is tokenization. The information is divided into small pieces (tokens) for correct work. Sentence tokenization breaks sentences into the text, while word tokenization breaks words into the sentences. Spaces and tokens of sentences separate word tokens by dots. 

Second one is stop word removal. All common words are removed from the text, and unique wordings remain.

NLP methods and systems

The fundamental methods of natural language processing are syntax and semantic analysis. The syntax is responsible for the correct arrangement of words in a sentence. Semantics is the study of the meaning and proper use of words. NLP uses syntaxes and semantics approaches for understanding the context and structure of sentences. 

Some words about syntax

The most common syntax technics:

  1. Parsing. It involves grammatical analysis of each sentence. The algorithm works with the sentence «The cat mewed». Parsing allows us to determine the parts of speech present in the sentence, i.e., a cat is a noun, and mewed is a verb. This procedure helps identify relationships between words.
  2. Word segmentation. The system takes a line of text and defines single words. Example: «People who practice a lot get higher scores». The algorithm can analyze the information and understand that spaces separate words.
  3. Sentence breaking. The method is necessary to define the boundaries of sentences within the text. For example, the algorithm accepts such text: The cat is hungry. I’m going to feed. The sentence break helps the machine understand the period separating sentences in. a text.
  4. Morphological segmentation. The method splits every long word into more minor elements, named morphemes. For example, the term «unbearably» can be divided into unbearably. The system recognizes morphemes un, bear, able and ly, this is especially helpful for machine translation.
  5. Lemmatization & Stemming. People often use words in different grammatical forms (inflected forms) when speaking or writing. AI uses such tools as stemming from making the text easier for the computer to recognize; they return the words to their root form. Words in their original form we call lemmas; this is the dictionary form of words. For example, all words «is, are, am, were, was» refer to the lemma «to be». As for stemming, the root form is called a stem. Stemming crops the words; that’s why sometimes there are semantic mistakes. For instance, the words «universal», «university», and «universe». All these words would result in the root form universe, which is wrong behavior.

Modern natural language processing is the form of AI, which researches and uses patterns in the database to improve understanding between humans and machines.

The meaning of semantic

The fundamental semantic methods:

  • Polygamy of words. The algorithm analyzes the contexts to understand the meaning of the word. For example, the sentence «The girl likes to eat bass». The system uses this method and understands that bass means a fish, not a frequency. 
  • Entity chunking. This technique divides words into groups. For instance, we have the sentence: «Jack started working for Acme Corp. in 2006». The algorithm determines the one-token person’s name, the company’s two-token name, and a temporal expression.
  • Natural language generation. The system uses the dataset to define words’ semantics and create a new text. The most frequent example is the automatically generating news articles.

The main tools of natural language processing include Natural Language Toolkit (NLTK), Gensim, and Intel NLP Architect.

Example NLP algorithms

Now, when understanding what is NLP, it is worth discussing its purpose. Today, we can meet NLP in various spheres of human life:

  • Email filters. Initially, email filters were not accurate, but thanks to a lot of practice, emails rarely end up in the wrong folder today.
  • Virtual assistants and smart speakers. Today, everyone knows who Apple’s Siri is. All voice assistants use natural language processing to analyze and understand voice commands. The use of intelligent assistants will increase exponentially in the future.
  • Virtual search engines. Whenever a user launches Google, he uses NLP algorithms. The system understands the meaning of words and the intent of the searcher.
  • Predictive text. You need to type just a few letters of the word on your smartphone, and the NLP algorithm will suggest the correct variant.
  • Chatbots. It’s the software that simulates human conversation. NLP tools help chatbots understand the sense of the sentences and define relevant topics and keywords; advanced chatbots recognize emotions.
  • Machine translation. Fast and competent translation of the text into different languages ​​is one of the main tasks of NLP.

It’s not a complete list of what you can’t do without NLP today.

Advantages of natural language processing

NLP has many benefits, but the main advantage of the technology is that it facilitates communication between humans and computers. The easiest way to control the machine is through computer code. The fact that the computer understands human speech makes contact with it more intuitive.

The list of other benefits of NLP that make any business more competitive:

  • Provide extensive analysis. Natural language processing allows the machine to work with a large amount of unstructured information, such as comments on social networks, letters to technical support, reviews, news, etc.
  • Automate processes in real-time. This technology can teach a machine to sort and transmit data without human involvement. All manipulations occur accurately, quickly, and around the clock.
  • You can adapt NLP techniques to different fields of activity. NLP tools can adapt even to complex industry terminology and recognize sarcasm words or expressions used figuratively.

NLP engineers report on new trends in NLP, so there will be a revolution in people-machine collaboration in the foreseeable future. 

The fundamental problems of NLP

There are a lot of problems with natural language processing; in most cases, they arise due to the ambiguity of human language:

  • Accuracy. Machines require humans to communicate with them in precise computer language; however, there are many inaccurate linguistic structures due to slang and dialect.
  • The tone of voice and inflection. Language processing does not always recognize sarcasm, and there are difficulties in determining the meaning of words in context.
  • The evolving use of language. NLP development is not as fast because the language (and how people use it) constantly changes.

Current active research should level many of the complexities.

Last words on natural language processing

Recently, the idea that a computer would understand human language seemed crazy. Despite this, NLP has become one of the most promising and rapidly growing technologies in a short period. Now every entrepreneur knows what is NLP. The development was made possible by research in linguistics and computer science.

NLP tools have become more and more accessible over the years. Companies create unique solutions and automate most business processes with the help of an NLP-based technician.