Compare and contrast AI with NLP and Machine Learning

NLP is a branch of Artificial Intelligence that studies how machines understand human language. Its goal is to build systems that sense the text and perform tasks like translation, grammar checking and topic classifications.

There is an increasing usage of NLP-equipped tools to gain insights from data and to automate routine tasks. For instance, this sentiment analyzer helps brands detect emotions in a text such as negative social media comments.

Natural Language Processing

NLP makes it possible for computers to understand human language. The most famous NLP examples are virtual assistants like Google Assist, Siri, and Alexa. NLP understands and translates a human language like “Hey Siri, where is the nearest gas station?” into numbers making it easy for machines to understand.

Another well-known NLP application is chatbots which helps to solve issues while performing natural language generation. Text recommendations when writing an email offers to translate a Facebook post written in a different language, or filtering unwanted promotional emails into your spam folder. 

In a nutshell, Natural Language Processing aims to make a human language which is complex, ambiguous, and incredibly diverse and comfortable for machines to understand.

How NLP works? 

It applies linguistics analyzing a grammatical structure and the meaning of words and use algorithms to build intelligent systems capable of performing a difficult task.

Difference between NLP, AI, Machine Learning

NLP, AI and ML use cross-wires interchangeably when differentiated between all three. The first to know is that NLP and machine learning are both subsets of AI. AI is an umbrella terminology for machines that simulates human intelligence. 

Artificial Intelligence encompasses systems that mimic cognitive capabilities, like learning from examples and solving problems. It covers a wide range of applications from self-driving cars to predictive systems.

NLP deals on how computers understand and translate the human language. Machines make sense of written or spoken text and perform tasks like translation, keyword extraction and topic classification.  

But to automate these processes and delivers accurate responses, machine learning is used. It is applying algorithms teaching machines how to learn and improve from experiences without being explicitly programmed automatically. 

For example, AI-powered chatbots use NLP to interpret what users say, and they intended to do, and machine learning delivers a much accurate response from past interaction.

NLP Techniques

NLP applies two techniques helping computers understand text: syntactic and semantic analysis.

1. Syntactic Analysis

Syntactic analysis or parsing ‒ analyzes text using basic grammar rules to identify sentence structure, how words organized, and how comments related to each other.

Some of the main sub-tasks include:

  • Tokenizations consist of breaking up a text into smaller parts called tokens to make the text easy.
  • Part of speech tagging labels tokens as a verbadverb, adjective, noun. It helps infer the meaning of a word. For example, the term “book” means different things if used as a verb or a noun).
  • Lemmatization and Stemming consist of reducing inflected words to their base form to make it easier to analyze. 
  • Stop-word removal frequently removes occurring words that don’t add any semantic value, such as I, they, have, like, yours.

Semantic Analysis

The semantic analysis focuses on capturing the meaning of the text. First, it studies the importance of each word. Then, looks at a combination of words and what they meant in context. 

These are the main sub-tasks of semantic analysis:

  • Word-sense disambiguation tries to identify which sense a term used in given contexts. 
  • Relationship extractions attempt to understand how entities (places, persons, organizations) relate to each other in a text.

Five Use Cases of NLP in Business 

NLP tools help understand how their customers perceive them across all communication channels, emails, product reviews, social media posts, surveys, and more.

AI tools used to understand online conversations and how customers talk about business automates repetitive and time-consuming tasks, increase efficiency, and enable workers to focus on more fulfilling jobs.

Some of the main applications of NLP business are: 

Sentiment Analysis

It identifies emotions in text and classifies opinions as positive, negative or neutral. It gains insights into how customers feel about brands or products by analyzing social media posts, product reviews or online surveys. 

For example, analyze tweets mentioning brands in real-time and detect angry customers’ comments accurately. It determines aspects customer service receive positive or negative feedback by analyzing open-ended responses to NPS surveys.

Language Translation

Machine translation technology has seen significant improvements over the past few years.

Translation tools enable the business to communicate with different languages improving their global communication or breaking into a new market.

This trains translation tools to understand specific terminology in any given industry like finance or medicine. So, inaccurate translations are standard with generic translation tools.

Text Extractions 

Text extraction enables to pull out pre-defined information from text. It deals with large amounts of data. This tool recognizes and extracts relevant keywords and features like product codes, colours, specs, and named entities like names of people, locations, company names, emails.

It uses text extraction to automatically find critical terms in legal documents, identify the main words mentioned in customer support tickets or pull-out product specification from a paragraph of text among other application.

Chatbot

Chatbots are AI systems designs to interact with humans with text or speeching. The use of chatbots raised. The ability to offer 24/7 assistance handles multiple queries and frees up human agents from answering repetitive questions.

Chatbots actively learn from each interaction and better understand user intent, relying on them to perform repetitive and simple tasks. If they come across a customer query, it cannot respond to pass onto human agents.

Topic Classifications

It helps to organize unstructured text into categories which gain insights from customer feedback. For example, analyzing hundreds of open-ended responses to NPS surveys. 

How many answers mention your customer support and What percentage of customers speak on price? 

Topic classifier for NPS feedback will tag all data in seconds.  

Topic classifications use to automate the process of tagging incoming support tickets and automatically route them to the right person.

Conclusion

NLP is a part of AI studies how machines interact with human language. NLP works behind the scenes to enhance tools used every day like chatbots, spell-checkers or language translators.

NLP, combined with machine learning algorithms, creates and learns systems to perform tasks independently for a complete experience. NLP-powered tools can help you classify social media posts by sentiment, or extract named entities from business emails, among other things. 

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All Comments

  • Avatar Mariamae Goodman

    This is an awesome information!

    3 weeks ago | 5 January, 2021 5:36 pm Reply
  • Avatar Mario Sousa

    #ONPASSIVE BLOG VIDEO: Compare and contrast AI with NLP and Machine Learning
    https://www.youtube.com/watch?v=r20p1fI9cqk

    3 weeks ago | 4 January, 2021 11:16 pm Reply

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