Other

Natural Language Processing (NLP): Concepts, Techniques, and Applications

0 purchase

Course
COMPUTER SCIENCE

Institution
Harvard University

This document explores Natural Language Processing (NLP), focusing on core concepts, techniques, and applications. It covers text preprocessing methods like tokenization, stemming, and lemmatization, along with advanced topics such as named entity recognition (NER), sentiment analysis, and speech r...

[Show more]

Preview 2 out of 6 pages

View example

Uploaded on February 1, 2025
Number of pages 6
Written in 2024/2025
Type Other
Person Unknown

machine learning
cs1004
text processing
tokenization
sentiment analysis
speech recognition
transformer models
word e
natural language processing
stemming and lemmatization
named entity recognition ner

$4.99

Also available in package deal from $56.79

Add to cart

Save

100% satisfaction guarantee
Immediately available after payment
Both online and in PDF
No strings attached

Also available in package deal (1)

Machine Learning & AI Complete Exam Study Pack (24 Documents)

$ 135.96 $ 56.79 24 items

1. Other - Foundations of artificial intelligence: key concepts and applications
2. Other - Types of artificial intelligence (ai): narrow, general, and superintelligent ai
3. Other - Introduction to machine learning: concepts, techniques, and applications
4. Other - The machine learning process: from data collection to model evaluation
5. Other - Machine learning algorithms: key models and techniques
6. Other - Applications of artificial intelligence: transforming industries and everyday life
7. Other - Deep learning: concepts and techniques
8. Other - Challenges in machine learning and artificial intelligence: overcoming barriers to pr...
9. Other - Key concepts in machine learning: foundations and techniques
10. Other - Supervised learning: concepts, algorithms, and applications
11. Other - Unsupervised learning: techniques, algorithms, and applications
12. Other - Reinforcement learning: concepts, algorithms, and applications
13. Other - Evaluation metrics in machine learning: measuring model performance
14. Other - Natural language processing (nlp): concepts, techniques, and applications
15. Other - Computer vision: concepts, techniques, and applications
16. Other - Ai and big data: concepts, technologies, and applications
17. Other - Ai in cloud computing: integration, benefits, and applications
18. Other - Ai and ethics: challenges, principles, and implications
19. Other - The future of ai and machine learning: trends, innovations, and challenges
20. Other - Real-world use cases of ai and machine learning
21. Other - Full guide on machine learning: concepts, algorithms, and applications
22. Other - Full guide on artificial intelligence: concepts, technologies, and future trends
23. Other - Question and answers on machine learning
24. Other - Question and answers on artificial intelligence
Show more

Natural Language Processing (NLP)
Natural Language Processing (NLP) is an interdisciplinary field that combines
computer science, artificial intelligence, and linguistics. The goal of NLP is to
enable computers to understand, interpret, and respond to human language in a
way that is both valuable and meaningful. NLP lies at the heart of many
applications we use daily, such as chatbots, speech recognition, machine
translation, and sentiment analysis.

The primary challenge of NLP is that human language is complex, ambiguous, and
varied. It involves parsing, understanding, and generating text or speech. NLP
techniques must address a variety of linguistic phenomena, including syntax
(structure of sentences), semantics (meaning of words and sentences), and
pragmatics (context in communication).

Applications of NLP
NLP is widely used in many industries for various applications, and its uses
continue to grow rapidly with advancements in AI and machine learning. Below
are some key applications:

1. Machine Translation
o NLP is fundamental in translating text from one language to another.
Services like Google Translate use NLP models to automatically
translate text between languages with increasing accuracy.
o Example: Google Translate, which uses deep learning and neural
networks, has vastly improved translations across numerous
languages, making cross-lingual communication easier than ever.
2. Sentiment Analysis
o Sentiment analysis is the process of determining the emotional tone
behind a body of text. It is used to understand the attitude, opinions,
or emotions expressed in written text, such as social media posts,
product reviews, and feedback surveys.
o Example: Companies use sentiment analysis to gauge customer
satisfaction and improve their services.

, 3. Chatbots and Virtual Assistants
o NLP is at the core of chatbots and virtual assistants like Siri, Alexa,
and Google Assistant. These systems understand spoken or typed
commands and respond with relevant information or actions.
o Example: Chatbots for customer service handle a variety of tasks,
from answering frequently asked questions to helping customers
with order tracking.
4. Speech Recognition
o Speech recognition systems convert spoken language into text. NLP
enables machines to understand and process spoken words, even
with different accents, dialects, and speech patterns.
o Example: Voice assistants, transcription services, and speech-to-text
software use NLP to transcribe conversations in real time.
5. Text Summarization
o Text summarization is the process of creating a concise version of a
long text document. It can be extractive, where key phrases are
selected, or abstractive, where a model generates a summary using
its own words.
o Example: News aggregation apps and email summary tools use text
summarization techniques to provide users with a quick overview of
lengthy articles.
6. Information Extraction (IE)
o Information extraction refers to automatically extracting structured
information from unstructured text. This is used to identify entities
(e.g., names, dates, locations) or relationships in documents.
o Example: Legal document analysis tools extract relevant facts,
entities, and relationships to streamline research and decision-
making.

Key Concepts in NLP
Several concepts and techniques form the foundation of NLP. These are essential
for understanding how NLP works and why it’s so powerful:

The benefits of buying summaries with Stuvia:

Guaranteed quality through customer reviews

Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.

Quick and easy check-out

You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.

Focus on what matters

Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!

Frequently asked questions

What do I get when I buy this document?

You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.

Satisfaction guarantee: how does it work?

Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.

Who am I buying these notes from?

Stuvia is a marketplace, so you are not buying this document from us, but from seller rileyclover179. Stuvia facilitates payment to the seller.

Will I be stuck with a subscription?

No, you only buy these notes for $4.99. You're not tied to anything after your purchase.

Can Stuvia be trusted?

4.6 stars on Google & Trustpilot (+1000 reviews)

66902 documents were sold in the last 30 days

Founded in 2010, the go-to place to buy study notes for 15 years now

Start selling