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What is Natural Language Processing (NLP)?

Natural Language Processing is the field of science that tries to maximize human-computer interaction using the language people use to communicate with each other or to strengthen communication between people using different languages . In the light of these developments, efforts are being made to give computers human characteristics.

So what is the purpose of Natural Language Processing, also known as NLP?

In general terms, we can define its purpose as eliminating the gap between computer and human communication. As a result of these developments, it makes it easier for computers to understand us and for us to understand them.

 Like humans, computers also have a native language. The difference here is that their language is a binary number system of ones and zeros, which means nothing to us linguistically unless translated. Natural Language Processing technology allows computers and humans to speak the same language.

History of Natural Language Processing Technology

The roots of natural language processing technology date back to the 1600s. It took three centuries of technological development for applicable examples of NLP to emerge.

NLP Comes to the Stage

The Georgetown IBM experiment conducted in 1954 was the first significant breakthrough in this field. This experiment, the first of its kind, involved automatic translation of more than 60 Russian sentences by computers.

Computers Learned to Understand

Artificial intelligence theorist and cognitive psychologist Roger Schank developed the conceptual dependency theory model for understanding natural language in 1969. Schank’s goal was to enable computers to read meaning independently of the actual written word.

 This approach taught machines that it doesn’t matter how sentences are written, as long as their meanings are the same, regardless of how they are entered into computer systems.

1970s: Fast Times of Technology

NLP researcher William A. Woods introduced the augmented relay network (ATN) in 1970. Representing natural language input, ATN gave computers the ability to analyze sentence structure regardless of complexity.

The 70s were spent with programmers developing the conceptual philosophy of existence (ontology). These studies aimed to develop a standard by introducing the formal representation of categories and relationships within a universal set of variables.

New Era in NLP

Natural language processing technology relied on hand-written rule-based models for most of the 80s. The first machine learning algorithms developed in the late 80s took shape as decision trees based on conditional rule mechanics such as “if X then Y,” which resemble complex handwritten rule sets. These NLP systems focused on statistical models that evaluated data inputs to a higher level of accuracy than previous systems.

The Golden Age of Natural Language Processing

Today, we see natural language processing technology in many areas of our daily lives. The new age technological explosion has led to the development of more applications for NLP than ever before. Natural language processing and artificial intelligence increase their functionality in direct proportion to the development of technology.

So How Is Natural Language Processing Applied?

Natural language processing processes vary from language to language. The computer first looks at the transformation of the word along with the suffixes on the root, this is called lexicology. 

After that, it tries to understand what the words in the sentence mean according to their order, this is called syntax. Then, it looks at what the sentence is trying to express in its essence, this is called semantics.

 Finally, it looks at what the sentences come together to express, and this is speech. In summary, the computer learns the context of the conversation by examining the root of the word separately, the order of the words separately, and the meaning of the sentence and speech separately and derives a meaning.

Today, deep learning , machine learning, statistical analysis and rule-based approaches are used in hybrid form to solve natural language processing problems . The problems studied can vary widely. 

Natural language processing comes into play in every field that touches on natural language, from correcting spelling errors to automatic translation systems, from language learning applications to personal assistant applications.

Natural Language Processing Usage Areas

Text mining, which has become very popular recently, is also included in natural language processing. Thanks to text mining, we can process the thoughts that accumulate in masses on the internet and extract meaning.

 Apart from this, natural language processing is used in speech recognition. Technologies such as speech recognition and automatic lip reading are used both to assist the hearing impaired and for surveillance.

 There are no microphones in CCTV cameras in order to avoid privacy violations , but some governments are trying to prevent a security problem that may arise by reading lips from CCTVs. Of course, there is also the ethical dilemma of personal privacy or public security here.

Smart Virtual Assistants

These systems, also known as chat bots, help people perform transactions by understanding the language they speak or write. This virtual assistant technology (Apple Siri, Google Assistant, etc.), which we started to use on mobile phones, is among the active research topics of natural language processing.

Information Retrieval and Information Extraction

Search engines such as Google, Yandex, Bing are information retrieval systems where natural language processing is used most intensively. 

Natural language processors are constantly working on new technologies so that these systems can better understand what information we want to search for and bring us the most appropriate data sources for the information we need.

Social Media Tracking

Large companies, administrators and decision makers need to regularly take the pulse of the public in the decisions they make, the advertisements they make and the policies they determine.

 Comments made on social media such as Facebook and Twitter through social media monitoring; analyzed with natural language processing methods; The general reactions, likes or dissatisfaction of the public can be followed.

Automatic Translation Systems

They are systems that automatically translate between different languages. This technology, which has entered our daily lives at full speed, takes its place among the important subjects of natural language processing and makes people’s lives easier.



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