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Deep Learning vs NLP

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Prerita Agarwal
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23 Jul, 2024 @ 01:30 PM

Introduction

It might be difficult to put our heads around complex terms like Machine Learning, Deep Learning, and Natural Language Processing (NLP) when we think of Artificial Intelligence. Deep Learning vs. NLP is one such frequently discussed topic. While Deep Learning and NLP both lie under the wide banner of Artificial Intelligence, the distinction between the two is quite apparent.

Deep Learning vs NLP

In this post, we'll look at the Deep Learning vs. NLP argument in depth, grasp their importance in the AI arena, examine how they interact, and learn various points on Deep Learning vs NLP.

What is Deep Learning?

Deep Learning is a subset of Machine Learning that uses Artificial Neural Networks (ANNs) to replicate the functioning of the human brain. Deep Learning refers to an Artificial Neural Network that is composed of an interconnected web of thousands or millions of neurons layered in numerous layers.

A neural network works like this: you input the neural network vast amounts of data, which are subsequently processed by the neurons. Each neuron has a function for activity. When a certain threshold is met, the neurons become active, and their values are distributed across the neural network.

Deep Learning is concerned with training enormous neural networks on massive amounts of data. Since everyday global data collection is off the charts right now (and will only increase in the future), Deep Learning presents an incredible opportunity. This is due to the fact that the more data you feed into a large neural network, the better it performs. Deep Learning is widely utilized in Predictive Analytics, Computer Vision, and Object Recognition. 

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What is Natural Language Processing (NLP)?

Natural Language Processing is an AI specialist field that tries to understand and depict the cognitive mechanisms that contribute to human language comprehension and generation. NLP is essentially a synthesis of Artificial Intelligence, Computer Science, and Linguistics. NLP strives to bridge the gap between computer comprehension and natural human languages through intelligent analysis of natural human languages.

NLP is concerned with programming computers to handle and evaluate vast volumes of written or verbal natural language data. It employs advanced approaches from Computational Linguistics, Artificial Intelligence, and Computer Science to assist computers in understanding, interpreting, and manipulating human languages. Since NLP allows computers and humans to communicate, we may obtain extraordinary results such as Sentiment Analysis, Information Extraction, Text Summarization, and Chatbots & Smart Virtual Assistants. 

What is Machine Learning?

Machine learning, sometimes known as ML, is a subfield of artificial intelligence that employs statistical techniques to solve enormous volumes of data without the need for humans. Machine learning solves problems in the same way that humans do but with vast amounts of data and automated procedures. 

Machine learning methods are more efficient in natural language processing, computer vision, and robotics. Machine learning is a technique for solving real-world AI challenges. Machine learning employs algorithms to educate machines to learn and improve with data without the need for explicit programming.

Is NLP Considered Machine Learning?

Machine Learning and natural language processing overlap in some parts. Natural language processing frequently uses machine learning as a tool. NLP also employs a variety of preprocessing approaches, such as:

  • Tokenizing: It is used to determine the most important parts of a sentence or words
     
  • POS Tagging: components of Speech Tagging, also known as entity extraction, is an ML technique that tags components of speech such as nouns, verbs, and so on
     
  • Entity Extraction: This Machine Learning technique is used for the extraction of entities from text data
     
  • Lemmatization and stemming: These processes compress words to their most basic form, making them easier to evaluate
     
  • Stop-word removal: This strategy eliminates commonly occurring terms that provide no semantic value to our study, such as I, they, have, and so on
Overlapping of ML, AI and NLP

Applying machine learning techniques to NLP problems would necessitate turning unstructured text data into structured data (often in tabular format). Machine learning for NLP entails applying statistical approaches to detect bits of speech, feelings, entities, and so on. These strategies are developed as a model and then applied to various text datasets. This is referred to as supervised learning. We may also employ a set of algorithms on massive datasets to extract patterns and make decisions. This is known as unsupervised learning.

Deep Learning vs NLP

Category  Deep Learning  NLP
Definition Deep learning is a branch of machine learning based on the concept of artificial neural networks, which instruct computers to learn through observation and repetition. Natural language processing refers to a computer software's capacity to grasp human language in its natural, spoken form.
Applications Image/speech recognition, NLP, recommendation systems, robotics, autonomous vehicles. Sentiment analysis, speech recognition, machine translation, text classification, and question answering.
Techniques Artificial neural networks, CNNs, RNNs, and GANs. Tokenization, stemming, lemmatization, part-of-speech tagging, and named entity recognition.
Performance State-of-the-art performance is challenging to interpret and requires significant resources. Achieves excellent performance, can be sensitive to nuances, and struggles with out-of-domain data.
Limitations "Black boxes" are difficult to interpret and explain. Sensitive to language/cultural nuances and struggles with out-of-domain data.
Examples Image classification, speech recognition, and autonomous vehicles. Sentiment analysis, named entity recognition, and machine translation.

Also see, Artificial Intelligence in Education

Frequently Asked Questions

Is NLP required in deep learning networks?

No. Deep learning algorithms make no use of NLP in any form. NLP stands for natural language processing and refers to computers' capacity to process text and analyze human language. Deep learning is the use of multilayer neural networks in machine learning.

What is Parsing in the context of NLP?

In NLP, parsing refers to a machine's knowledge of a sentence and its grammatical structure. Parsing enables the machine to comprehend the meaning of a word in a sentence as well as the arrangement of words, phrases, nouns, subjects, and objects in a sentence.

What is Pragmatic Analysis?

Pragmatic analysis is an important procedure in NLP for interpreting knowledge that exists outside of a specific document. The goal of using pragmatic analysis is to focus on a different aspect of a document or text in a language. This necessitates a thorough understanding of the real world.

What is Feature Extraction in NLP?

The features or properties of a word help in text or document analysis. They also help in the sentiment analysis of a text. Feature extraction is one of the approaches employed by recommendation systems.

Conclusion

Deep Learning and Natural Language Processing are both subsets of the greater field of Artificial Intelligence. While NLP is changing how machines interpret human language and behavior, Deep Learning is expanding NLP's applications. In this article, we briefly examined what deep learning and NLP are, along with various points on  Deep Learning vs NLP.

Recommended Reading:

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To better understand the topic, you can refer to Introduction to Natural Language ProcessingBaby Name Generation with Deep Learning and NLP, and Restaurant Review Analysis Using NLP.

You can also consider our Machine Learning Course to give your career an edge over others.

Topics covered
1.
Introduction
2.
What is Deep Learning?
3.
What is Natural Language Processing (NLP)?
4.
What is Machine Learning?
5.
Is NLP Considered Machine Learning?
6.
Deep Learning vs NLP
7.
Frequently Asked Questions
7.1.
Is NLP required in deep learning networks?
7.2.
What is Parsing in the context of NLP?
7.3.
What is Pragmatic Analysis?
7.4.
What is Feature Extraction in NLP?
8.
Conclusion