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. 

    <!–

  • ONPASSIVE
  • ONPASSIVE
  • 3 January, 2021
  • –>

  • 433 Views
  • 190 1

<!–

–> Source

NOT to be Missed Hurry Up!

Leave a Reply