This document discusses natural language processing and provides information about its history, applications, techniques, opportunities, limitations, and future. It introduces natural language processing as a sector of artificial intelligence that can analyze human language. It then discusses the Stanford NLP group and some well-known current applications of NLP like voice search, translation, information retrieval, and chatbots. The document also covers specific NLP techniques like tokenization, part-of-speech tagging, and sentiment analysis. It discusses opportunities in research and jobs, current limitations, and predictions for the future growth of NLP in applications like conversational agents, search, and deriving intelligence from unstructured data.

1. Natural Language Processing

2. #TeamNoFrazzle

3.  NAZMUL AHSAN
151-15-4668
 MAHBUBUR RAHMAN
151-15-4761
 FARHAN TAWSIF CHOWDHURY
151-15-4705
 SANZIDUL ISLAM
151-15-5223
 SADIA SULTANA SHARMIN MOUSUMI
151-15-5191

4. Natural language processing is a sector of artificial
intelligence that can analysis human language.

5. Background history
:
CAN
MACHINE
THINK ?

6. Stanford NLP GROUP :
• A group from Stanford university developed some software.
• One of them is Core Stanford NLP .written in Java.
• Packages are widely used in industry, academia, and government.

7. Well known now-a-days :
• Voice search
• Translator
• Information retrieval
• Captcha Challenge
• Different types of App.
• IOT Internet Of Things

9. WHY
Natural Language
Processing ?

10. Retrieve Information:
My friend: When you will meet with me?
Me: I have decided to meet tomorrow at 10:00 AM in Library

11. Retrieve Information:
My friend: When you will meet with me?
Me: I have decided to meet tomorrow at 10:00 AM in Library
Date : 31-03-17 (Tomorrow)
Time: 10:00 AM
Place: Library

12. Retrieve Information:
Event: Database Presentation
Time: 11:30 AM
Info: Take preparation properly

13. MachineTranslation:
1. Google Translation.
2. Pipilika (first Bangla search engine)
3. Voice speech to Text

14. আমি ত োিোকে ভোলবোমি | => I love you.
Grass is greener on the other of the side. => নদীর ওপোকের ঘোিগুচ্ছ তবশী িবুজ।
Google’s Translation : ঘাস নদী ওপারে সবুজ |
MachineTranslation:

15. Language Processing:
Generate SQL from natural language.
Natural Language: Publish department
SQL Query: Select * from department
Natural Language: Show all student information of Section E, 40 batch,
CSE
SQL Query: Select * from CSE_students where batch = `40` AND section
= `E`

16. NLPTechnique:
1. One of the Machine Learning application is NLP
2. Two approach:
I. Supervised.
* Tag key-word work as training set
* YouTube Suggestion, Favorite list in messenger.
II. Unsupervised.
* No pre-training set.
* Clustering, Data analyzation, Google Search

17. “First we thought the PC was a calculator.
Then we foundout how to turn numbers into letters withASCII — and
we thought it was a typewriter. Then we discoveredgraphics, andwe
thought it was a television. Withthe WorldWide Web, we've realized
it's a brochure.”
― Douglas Adams

18. • Add your results here
Results
• Add your objective here
Objective
Project Description

31. Aspects of NLP
• Tokenization / Segmentation
• Disambiguation
• Stemming
• Part of Speech (POS) tagging
• Contextual Analysis
• Sentiment Analysis

32. Tokenization & Segmentation
• Segmenting text into words
“The meeting has been scheduled for this Saturday.”
“He has agreed to co-operate with me.”
“Indian Airlines introduces another flight on the New Delhi–
Mumbai route.”
“We are leaving for the U.S.A. on 26th May.”
“Fahad is playing the role of Duke of Athens in A Midsummer
Night’s Dream in a theatre in New York City!”
• Named Entity Recognition

33. POS tagging
• Part of speech (POS) recognition
“ Today is a beautiful day. “
Today is a beautiful day
Noun Verb Article Adjective Noun

34. Showing application of NLP
with python scripting

35. Opportunities of NLP
• Research opportunity
• Employment/Job opportunity

36. Limitation of NLP

37. Future of NLP
• The bots
• Supporting invisible UI
• Smarter search
• Intelligence from unstructured information

41. Do you have Any QUESTION?