Natural Language Processing & AI: Methodology and Correlation Explained

Natural Language Processing & AI Methodology and Correlation Explained

Natural language processing (NLP) refers to the branch of artificial intelligence that is concentrated on computers to analyze and understand written text and spoken words like a human being. NLP is widely used in businesses nowadays in a variety of different ways.

NLP blends statistical machine learning models with computational linguistics (rule-based modeling of human language). These technologies are used to enable a computer to process human language in a way to understand what is said or written by a human speaker and writer.

NLP is helpful in many scenarios like it can be used as a translator that translates text while considering the sentiments and intentions of the writer or speaker, summarizing extensive data of text and responding to written or spoken commands.

You might have seen NLP in action in different ways. It is used in specific GPS systems, digital assistants like Alexa, paraphrasing tools like, chatbots on various websites for customer service.

Alt-text (Paraphrasing tool)

Also, you will find many online tools that use NLP and other AI algorithms to generate, analyze, edit, classify, and summarize text like an essay generator that crafts essays in seconds when a topic is given, plagiarism checker tools that analyzes a text provided, and finds duplicate content by scanning the text against billions of webpages.

Essay Generator

NLP is used mainly to address and simplify hard-to-do and effort-consuming business processes, reduce manpower, and used to help in increasing the productivity of employees.

Components of NLP

NLP comprises two different parts, here is each one discussed below:

Natural language generation

Natural language generation, or NLG  as the name implies is the process of crafting phrases and sentences with proper meaning from data. This method has three stages:

  1. Text planning: analyzing and recovering applicable text
  2. Sentence planning: generating phrases and sentences with a specific tone.
  3. Text realization: Mapping to deduce proper sentence structures.

NLG is used in chatbots, analytics platforms, paraphrase tools, sentiment analysis, virtual assistants, and transcription tools.

Natural language understanding

Natural language understanding or NLU is used to extract metadata from content to analyze, interpret and understand human language. It is used to:

  • Help in analyzing various aspects of the language used
  • Helps in the right presentation of input content in natural language.

As compared, NLU is more complex than NLG-based tasks owning to referential:

●     Lexical ambiguity:

A word sometimes has different meanings ultimately changing the whole content of the text. For instance, “Alex saw a bat”. This sentence holds different meanings and the idea is not clear. Alex saw a bat (a bat can be either an animal or a wooden bat) also Alex saw a bat could mean that Alex was sawing a baseball or cricket bat using a saw.

●     Syntactic ambiguity:

Syntactic ambiguity is referred to when a number of words aligned in a sentence have more than one meaning. For example, “I invited the person with a note” in this sentence there are two meanings, either the person is invited using a note or the person holding the note is invited.

●     Referential ambiguity:

This ambiguity occurs when a word or phrase deduces two or more properties causing confusion. For instance, Stella met Alice and Stephanie, and later they went to see a movie. Here “they” refers to either the three of them going to watch the movie or Alice and Stephanie after meeting Stella going to watch the movie.

Application Areas of NLP

NLP has broad use. Some of NLP uses are discussed below:

Automatic translation

You might have seen translators; they are powered by NLP algorithms that help translate one natural language to another. The resulting text is fluent, and the original text’s meaning is preserved.

Paraphrasing of Text

Paraphrasing requires in-depth knowledge and practice for effective results. NLP, with other AI-based algorithms, tokenizes the words, changes their synonyms, detects the tone and rewrites the text using different techniques to get you a unique and compelling text as a result.

You will see many NLP-based paraphrasing tools available for such uses. If you want to use one, you can use as it is one of the mainstream and reputable tools that is specifically built to fulfill paraphrasing-related needs.


Named-Entity Recognition

NLP is used to identify and extract entities like people, things, and names of locations which are further categorized as time, place, person name, and company. The name speaks much about its use.

Typical uses are research for academic copies, content categorization for SEO, and more. You can see as a prime example that uses name-entity recognition in its tools to create effective invoices and much more.

Sentiment analysis

Humans’ way of thinking is unpredictable, and thus it makes it hard for a computer to understand human language and act accordingly. This is where NLP comes in. It is used in sentiment analysis, which determines whether a specific text is either positive, neutral, or negative. Sentiment analysis detects trends, customer reviews, product hunting, and more.

Resolving queries

NLP analyzes unstructured documents from sources like Wikipedia, various random articles, tweets, and quora answers to automatically gather all the helpful information to answer a user’s questions.

Methodology of Natural Language Processing in AI

The NLP pipeline comprises a set of steps to read and understand human language.

NLP is a branch of AI that reads and understands human language and acts accordingly. So, here are the series of steps in NLP-based processes to understand the text and audio-based content.

Sentence Segmentation or Division

This is the first step in NLP where it analyzes a paragraph and separates it into different sentences to make the text easier to understand.

For example, “The Witches’ Water is a theme park at the middle station of the cable car at Söll in Tyrol, Austria. The central part is a water park consisting of ponds and rivulets where children can play a variety of water games. It extends over an area of about 500 meters and is complemented by various alpine restaurants, playgrounds and a petting zoo.”

Source: Wikipedia

After segmentation, here are the results we’ll get:

  1. “The Witches’ Water is a theme park at the middle station of the cable car at Söll in Tyrol, Austria.”
  2. “The central part is a water park consisting of ponds and rivulets where children can play a variety of water games.”
  3. “It extends over an area of about 500 meters and is complemented by various alpine restaurants, playgrounds and a petting zoo.”

Word tokenization

After breaking a paragraph into sentences, word tokenization is used to separate each word (tokens) from a sentence to help the machine understand the context or idea behind the text.

Here we will use one sentence from the above example: “The Witches’ Water is a theme park at the middle station of the cable car at Söll in Tyrol, Austria.”

After tokenization:

“The”, “Witches”, “Water”, “is”, “a”, “theme”, “park”, “at”, “the”, “middle”, “stallion”, “of”, “the”, “cable”, “car”, “at”, “Söll”, “in”, “Tyrol”, “Austria”,“.”

Stemming and Lemmatization

The third step is stemming the text in which the model analyzes parts of speech to comprehend and extract the actual meaning or context of the sentence. After tokenization, the words are separated which are then used by stemming and normalized into their root form. The base or root form of words or tokens helps in predicting the parts of speech.

On the other hand, lemmatization is done to return a word to its canonical form by removing certain inflectional ends. The resulting word is called a lemma. However, both of the processes seem to share a lot of similarities and the only difference is that stemming generated words sometimes have no meaning while lemmas are actual words.

Stemming vs Lemmatization

Stop word analysis

The important words in documents are extracted and focused mainly by NLP and words like “is” “am” “the” “a” are flagged as stop words by NLP. These words are filtered mainly to ensure more focus on the words that deduce a meaning for better context understanding of the text.

Dependency parsing

The next step in the pipeline is dependency parsing where the relation of words in a sentence is defined to clear the idea of the text. A tree is built where one word is the parent word and the main verb is assigned as root conjunction.

Dependency parsing

Part-of-speech (POS) tagging

To clarify the text and is understandable by the computer, each word is tagged with its parts of speech. This way the grammatical issues are avoided and the true meaning of the sentence is indicated. Here is an example:

Part-of-speech (POS) tagging 1

Part-of-speech (POS) tagging 3

All these processes in the pipeline are relevant to each other and it might look like a long process but as this is done using machines, it takes seconds to do all these tasks, thanks to pre-trained models.

Closing Words

NLP is a study and part of artificial intelligence that is used to help machines understand human language and interact accordingly. It is used widely at the backend of many tools we use in our day-to-day routine like grammar checkers, translators, and paraphrasing tools.

NLP doesn’t always perform alone, it is now being used with machine learning algorithms to train machines to perform hectic tasks without any assistance and get better from each task.

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