Natural language system

1. BY JOHN P SEBY
NATURAL LANGUAGE PROCESSING

2. What is NLP (Natural Language Processing)?
•NLP stands for Natural Language Processing, which is a branch of
artificial intelligence (AI) that focuses on enabling computers to
understand, interpret, and generate human language in a way that is
both meaningful and valuable.
•NLP is an interdisciplinary field that uses computational methods to:
Investigate the properties of written human language and model the
cognitive mechanisms underlying the understanding and production of
written language.
Develop novel practical applications involving the intelligent
processing of written human language by computer.

3. NLP plays a big part in Machine learning techniques:
automating the construction and adaptation of machine
dictionaries modeling human agents' desires and beliefs.
o essential component of NLP
o closer to AI

4. 1.Why is NLP important?
1.NLP is crucial because it allows computers to interact with humans in
natural language, making technology more accessible and user-
friendly. It enables applications like chatbots, translation services,
and sentiment analysis, enhancing user experiences.
NLP Applications:
What are some practical applications of NLP?
NLP has various applications, including:
1.Sentiment Analysis : Determining the emotional tone of text data.
2.Chatbots and Virtual Assistants: Providing automated conversational
interfaces.

5. •Machine Translation: Translating text from one language to another.
•Information Extraction: Extracting structured data from unstructured text.
•Text Summarization: Generating concise summaries from lengthy texts.
How NLP Works:
1.What are the core processes in NLP?
1.NLP involves several key processes, including: Tokenization: Dividing text
into individual words or tokens.
2.Part-of-Speech Tagging: Assigning grammatical tags (e.g., noun, verb) to
words.
3.Named Entity Recognition: Identifying and categorizing entities (e.g., names,
dates) in text.
4.Syntax Analysis: Parsing the grammatical structure of sentences.
5.Semantic Analysis: Understanding the meaning of words and phrases in
context.

6. NLP Challenges:
What are some challenges in NLP?
NLP faces several challenges, such as:
1.Ambiguity in Language: Words and phrases can have multiple meanings.
2.Handling Multiple Languages: NLP needs to work with various languages.
3.Context Understanding: Grasping the context in which language is used.
4.Dealing with Slang and Informal Language: Understanding colloquial
expressions.

7. Recent Advances in NLP:
What are some recent breakthroughs in NLP?
1.Recent advances in NLP include:
2.Pre-trained language models like BERT and GPT-3, which have
improved language understanding.
3.Transfer learning techniques that allow models to generalize from one
task to another.
4.Multimodal NLP, which combines text and image understanding for richer
AI applications.

8. Future of NLP:
1.What can we expect in the future of NLP?
1.The future of NLP holds promise for : Improved language models with even
better understanding and generation capabilities.
2.Expanding NLP to support low-resource languages and dialects.
3.Heightened focus on ethical considerations in NLP, including bias mitigation.
4.Integration of NLP with other AI domains, such as computer vision, for more
comprehensive AI systems.

9. EXAMPLE
• Email filters
• Smart assistant
• Search results
• Language translation

10. THANK YOU
BY
Dominic rozario.I
21ucs506