Natural Language Processing (NLP) is a branch of artificial intelligence that enables computers to understand, interpret, and generate human language. NLP analyzes text to determine meaning and relationships between words in order to automatically perform tasks like translation, information extraction, and sentiment analysis. Common applications of NLP include virtual assistants, chatbots, language translation, text extraction, and sentiment analysis of customer feedback.

1. Artificial
Intelligence

2. Natural Language
Processing

3. Natural Language Processing
 A branch of Artificial Intelligence (AI) that enables machines
to understand the human language.
 Its goal is to build systems that can make sense of text and automatically
perform tasks like translation, spell check.

4. Natural Language Processing
 NLP analyzes the grammatical structure of sentences and the individual meaning of
words, then uses algorithms to extract meaning and deliver outputs.
outputs.
 It makes sense of human language so that it can automatically perform different tasks.

5. Example
NLP in action are virtual assistants, like Google
Assist, Siri, and Alexa.
NLP understands written and spoken text like
“Hey Siri, where is the nearest gas station?”
and transforms it into numbers, making it easy for machines
to understand.

6. Example
Another well-known is chatbots.
They help support teams solve issues
by understanding
common language requests and
responding automatically.

7. NLP Techniques
Natural Language Processing (NLP) applies two techniques to help computers
understand text:
 Syntactic Analysis
 Semantic Analysis.

8. Syntactic Analysis
 Syntactic analysis analyzes text using basic grammar
rules to identify sentence structure.
 How words are organized, and how words relate to each other.

9. Syntactic Analysis
Sub-tasks of syntactic analysis:
 Tokenization
 Part of speech tagging (PoS tagging)
 Lemmatization & stemming
 Stop-word removal

10. Syntactic Analysis
 Tokenization :
 It consists of breaking up a text into smaller parts
sentences or words) to make text easier to handle.
 Part of speech tagging:
 It labels tokens as verb, adverb, adjective, noun, etc. This helps
word (for example, the word “book” means different things if

11. Syntactic Analysis
 Lemmatization & stemming:
 It consist of reducing inflected words to their base form to
analyze.
 Stop-word removal:
 It removes frequently occurring words that don’t add any
they, have, like, yours, etc.

12. Semantic Analysis
 Semantic analysis focuses on capturing the meaning of text.
 First, it studies the meaning of each individual word.
 Then, it looks at the combination of words and what they
mean in context

13. Semantic Analysis
Sub-tasks of semantic analysis:
 Word sense dis-ambiguation
 Relationship extraction

14. Semantic Analysis
 Word sense disambiguation:
 It tries to identify in which sense a word is being used in each
 Relationship extraction:
 It attempts to understand how entities (places, persons,
each other in a text.

15. Use Cases of NLP in Business
 NLP tools are helping companies understand how their customers perceive them across all
channels of communication, whether emails, product reviews, social media posts, surveys,
and more.
 Not only can AI tools be used to understand online conversations and how customers are
talking about businesses

16. Sentiment
Analysis
Sentiment analysis identifies
emotions in text and classifies
opinions as positive, negative, or
neutral.

17. Sentiment Analysis
 By analyzing social media posts, product reviews, or online surveys,
companies can gain insight into how customers feel about brands or products.
 For example, you could analyze tweets mentioning your brand in real-
time and detect comments from angry customers right away.

18. Language Translation
 Translation tools enable businesses to communicate in different languages, helping them
improve their global communication or break into new markets.
 You can also train translation tools to understand specific terminology in any given industry,
like finance or medicine. So you don’t have to worry about inaccurate translations that are
common with generic translation tools.

19. Text Extraction
Text extraction enables you to pull out pre-defined information from text.
If you deal with large amounts of data, this tool helps you recognize and extract
relevant keywords and features (like product codes, colors, and specs),
and specs),
And name entities (like names of people, locations, company names, emails,
etc).

20. Text Extraction
 Companies can use text extraction to automatically
find key terms in legal documents.
 Identify the main words mentioned in customer support tickets, or pull out product.
 Specifications from a paragraph of text, among many other
applications.

21. Chatbots
 Chatbots are AI systems designed to interact with humans through text or
speech.
 The use of chatbots for customer care is on the rise, due to their ability to
offer 24/7 assistance (speeding up response times), handle multiple queries
simultaneously, and free up human agents from answering repetitive
questions.

22. Chatbots
 Chatbots actively learn from each interaction and get better at understanding
user intent, so you can rely on them to
perform repetitive and simple tasks.
 If they come across a customer query, they’re not able to
respond to, they’ll pass it onto a human agent.

23. Natural Language Processing
 Natural Language Processing (NLP) is the part of AI that
studies how machines interact with human language.
 NLP works behind the scenes to enhance tools we use every
 day, like chatbots, spell-checkers, or language translators.

24. Natural Language Processing
Combined with machine learning algorithms, NLP creates
systems that learn to perform tasks on their own and get
better through experience.
NLP-powered tools can help you classify social media posts by
sentiment, or extract named entities from business emails,
among many other things.