What is Natural Language Processing (NLP)? How does NLP work? And its Applications.

Natural language processing is a branch of Artificial Intelligence that enables computers to understand, process, and generate language just like people do — and its use in business is rapidly growing. It became an integral part of our daily lives: Whether we’re asking our smartphone for directions or engaging with Alexa or Google, Natural language processing and its sub-categories are hard at work behind the scenes, translating our voice or text input and providing an appropriate voice or text output.

What is Natural Language Processing (NLP)? How does NPL work? And its Applications.

Natural Language Processing Definition

Natural language processing is the ability of a computer program to understand the human language as it is spoken and written — referred to as natural language.

Natural language processing is the branch of AI that deals with training a computer to understand, process, and generate language. Search engines, machine translation services, and voice assistants are all powered by these technology.

While the term originally referred to as the system’s ability to read, since it become a colloquialism for all computational linguistics. The subcategories include natural language generation (NLG) — a computer’s ability to create communication of its own — and natural language understanding (NLU) — the ability to understand the slang, mispronunciations, misspellings, and other variants in the language.

Natural language processing has existed for more than 50 years and has roots in the field of linguistics. It has a variety of real-world applications in several fields, that includes medical research, search engines and business intelligence.

How does Natural Language Processing work?

“Natural language processing works through machine learning systems that store words and the ways they come together just like any other form of data. The Phrases, sentences, and sometimes entire books are fed into ML engines where they’re processed using grammatical rules, people’s real-life linguistic habits, or both. The computer uses this data to find out the patterns and extrapolate what comes next.”

NLP enables computers to understand the natural language as humans do, Whether the language is spoken or written, natural language processing uses AI to take real-world input, process it, and make a sense of it in a way a computer can understand. Every humans has different sensors — such as ears to hear and eyes to see — computers have programs to read and microphones to collect audio. And just like humans have a brain to process that input, computers have a program to process their respective inputs. At some point in processing, the input is converted the code to computer can understand.

Two Main Phases in Natural Language Processing: Data Preprocessing and Algorithm Development.

Data Preprocessing involves preparing and cleaning text data for machines to be able to analyze it. The preprocessing puts data in the workable form and highlights features in the text that an algorithm can work with.

Tokenization. When the text is broken down into smaller units to work with.
Stop word removal. This is when the common words are removed from text, so unique words that offer the most information about the text remain.
Lemmatization and stemming. This is when the words are reduced to their root forms to process.
Part-of-speech tagging. This is when the words are marked based on the part-of-speech they are nouns, verbs and adjectives.

Once the data has been preprocessed the algorithm is developed and process it. There are many different natural language processing algorithms, but two main types are commonly used:

Rules-based system. In this system uses carefully designed linguistic rules. This approach were used early on in the development of natural language processing and is still used.
Machine learning-based system. ML algorithms use statistical methods, in which they learn to perform tasks based on training data they are fed and adjust their methods as more data is processed. Using the combination of machine learning, deep learning and neural networks, natural language processing algorithms hone their own rules through repeated processing and learning.

NLP Applications

Machine translation is the onw of the most powerful NLP application. Every time you look something up with Google or Bing, you’re data’s are feeding into the system. When you click on to the search result, the system interprets and the confirmation the results that it has found are correct and uses this information to better search in the future.

Chatbots works in the same way: It integrate with Slack, Microsoft Messenger, and other chat programs where they read the language that you use, then turn on when you type in a trigger phrase. Voice assistants like, Siri and Alexa also kick into the gear when they hear phrases like “Hey, Alexa.”

Techniques and Methods of Natural Language Processing

Semantic and Syntax analysis are two main techniques used with natural language processing.

The syntax, an arrangement of words in a sentence to make grammatical sense. Natural Language Processing uses syntax to assess meaning from a language based on grammatical rules. Syntax techniques include:

Parsing. It is the grammatical analysis of a sentence. Example: A NLP algorithm is fed the sentence, “The dog barked.” Parsing involves breaking the sentence into parts of speech — i.e., dog = noun, barked = verb. This is useful for more complex downstream processing tasks.

Word segmentation. It is the act of taking a string of text and deriving word forms from it. Example: Person scans a handwritten document into a computer. The algorithm would be able to analyze the page and recognize the words and are divided by white spaces.

Sentence breaking. The places sentence boundaries in large texts. Example: A NPL algorithm is fed the text, “The dog barked. I woke up.” The algorithm can recognize the period and that splits up the sentences using sentence breaking.

Morphological segmentation. It divides the words into smaller parts called morphemes. Example: The untestable word would be broken into [[un[[test]able]]ly], where the algorithm recognizes “un,” “test,” “able” and “ly” as morphemes. It is especially useful in machine translation and speech recognition.

Stemming. It divides the words with inflexion in them to root forms. Example: In the sentence, “The dog barked,” this algorithm would be able to recognize the root of the word “barked” is “bark.” This would be very useful if a user was analyzing the text for all instances of the word bark, as well as all of its conjugations. The algorithm can be see that they are essentially the same word even though the letters are different.

Semantics involves the use in the meaning behind words. NLP applies the algorithms to understand the meaning and structure of sentences. Semantics techniques include:

Word sense disambiguation. It derives the meaning of a word based on context. Example: The sentence consider, “The pig is in the pen.” The word pen has different meanings with using algorithm this method can understand that the use of the word pen here refers to a fenced-in area, not a writing implement.

Named entity recognition. It determines words that can be categorized into groups. Example: An algorithm using this method could analyze the semantics of the text, it would be able to differentiate between entities that are visually the same. In this sentence, “Daniel McDonald’s son went to McDonald’s and ordered a Meal,” the algorithm can recognize the two instances of “McDonald’s” as two separate entities — one a restaurant and one a person.

Natural language generation. This uses a database to determine the semantics behind words and generate a new text. Example: The algorithm automatically write a summary of findings from a business intelligence platform, mapping certain words and phrases to features of the data in the BI platform.

Natural Language Processing Software

Using NLP we can build chatbot, voice assistant, predictive text application, or other application. Its core, you’ll need tools to help you do it. According to Technology Evaluation Centers, the most popular software includes:

Natural Language Toolkit (NLTK). NLTK is an open-source framework that build Python programs to work with human language data. It was developed in the Department of Computer and Information Science at the University of Pennsylvania and also provides the interfaces to more than 50 corpora and lexical resources, a suite text processing libraries, wrappers for natural language processing libraries, and a discussion forum. NLTK was offered under the Apache 2.0 license.

SpaCy. It is an open-source library for advanced NPL licensed by MIT. And it explicitly designed for production use rather than research. SpaCy also made a high-level data science in mind and allows deep data mining.

Gensim. Gensim is an platform-independent open-source Python library supports scalable statistical semantics, analysis of plain-text documents for semantic structure, and the ability to retrieve semantically similar documents. It’s can handle large amounts of text without human supervision.

Amazon Comprehend. The Amazon service doesn’t require a machine learning experience. It’s intended to help the organizations to find insights from email, customer reviews, social media, support tickets, and other text. It uses sentiment analysis, part-of-speech extraction, and tokenization to parse the intention behind the words.

IBM Watson Tone Analyzer. This is a cloud-based solution is intended for social listening, chatbot integration, and customer service monitoring. It can also analyze the emotion and tone in customer posts and monitor customer service calls and chat conversations.

Google Cloud Translation. This API uses Natural language processing examine the source text to determine language and then use neural machine translation to dynamically translate the text into another language. This API allows users to integrate the functionality into their programs.


Natural language processing plays a vital role in the technology and the way humans interact with it. NPL is used in many real-world applications in both the business and consumer spheres, including chatbots, cybersecurity, search engines and big data analytics. Though not without its challenges, Natural language processing is expected to continue to be an important part of both industry and everyday life.


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