Natural Language Processing (NLP) refers to the ability of computers to understand and interpret human language, employing machine learning algorithms for analysis. It has various applications such as voice assistants and text analysis tools, while enabling functionalities like sentiment analysis and text searching. However, NLP faces challenges in understanding abstract and nuanced language, such as sarcasm, highlighting its limitations.

1. Natural Language
Processing (NLP)
Presented by Aritra Mukherjee
Techno International Batanagar
Computer Science & Engineering | 3rd Year

2. Introduction

3. Hey Google
“is it going to rain today?”
“do I need an umbrella today?”

4. What is NLP?
Natural language processing (NLP) is the
ability of a computer program to
understand human language as it is
spoken.
NLP is a component of artificial intelligence.

5. Programming Languages
C++, Java, Python …
English, Hindi, Bengali …
Natural Languages

6. Computers traditionally require
humans to "speak" to them in a
programming language that is
precise, unambiguous and
highly structured.

7. Human speech, is not
precise - it is often
ambiguous and the
linguistic structure can
depend on many complex
variables, like regional
dialects and social
context.

8. How does NLP work?

9. Current approaches to NLP
are based on machine
learning algorithms, a
subset of AI that examines
and uses patterns in data
to improve a program's
understanding.

10. Techniques used in NLP

11. 1. Syntax
In NLP, syntactic analysis is used
to assess how the natural
language aligns with the
grammatical rules.

12. 2. Semantics
Semantics refers to the meaning
that is conveyed by text and
involves computer algorithms to
interpret the same.

13. “CSK was on fire last Sunday,
they totally destroyed KKR”
• To a computer, this may mean CSK was literally on fire.
• CSK literally destroyed KKR and it doesn’t exist anymore!

14. Applications of NLP
(some of the many)

15. 1. Voice Based
Google Translate Google Assistant Siri

16. 2. Text Based
SwiftKey Keyboard Microsoft Word Grammarly

17. Benefits of NLP

18. 1. Sentiment Analysis
Enables data scientists to assess
comments on social media to see
how their business's brand is
performing.

19. 2. Searching Text
NLP allows analysts to sift
through massive troves of text to
find relevant information.
Enterprise Search | Text Summarization | Filtering Sensitive Keywords

20. Limitations of NLP
(understanding abstract rules)

21. 1. High Level Rules
Sometimes, words can be high-
level and abstract; for example,
when someone uses a sarcastic
remark to pass information.

22. 2. Low Level Rules
Some of the rules can be low-
levelled; for example, using the
character “s” to signify the
plurality of items.

23. Current Research Activities

24. 1. Generation of a natural language
resource using a parallel corpus
2. SystemT: Declarative Text
Understanding for Enterprise
Alan Akbik, Laura Chiticariu, Marina Danilevsky,
Yunyao Li, Huaiyu Zhu
Laura Chiticariu, Marina Danilevsky, Yunyao Li,
Frederick Reiss, Huaiyu Zhu

25. References

26. 1. Wikipedia -
https://en.wikipedia.org/wiki/Natural_language_processing#Syntax
2. Search Business Analytics -
https://searchbusinessanalytics.techtarget.com/definition/natural-language-
processing-NLP
3. Becominghuman.ai - https://becominghuman.ai/a-simple-
introduction-to-natural-language-processing-ea66a1747b32

27. Thank You