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AI in Natural Language Processing: Advancements and Applications

Last updated on: 
February 14, 2024
|
by 
Isha Chakraborty
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About The Author!
Isha Chakraborty
Isha Chakraborty

Natural Language Processing (NLP) has become the heartbeat of the AI market and is set to skyrocket at a staggering rate. It’s time to brace yourselves for a journey from $24.10 billion in 2023 to a surprising $112.28 billion by 2030. 

Advancements in NLP, particularly through deep learning and transformer-based language models such as GPT-3 and BERT, have not only transformed human-machine interactions but have also opened up new horizons across sectors like customer support, education, healthcare, and finance.

In this article, you will acknowledge the key advancements in NLP and their practical applications, empowering you to harness the full potential of this emerging technology.

Here’s what we’ll cover in this article:

  • What is Natural Language Processing in AI?
  • Advancements in Natural Language Processing
    • Transformer-Based Models
    • BERT
    • GPT-3
    • Transfer Learning
    • Multimodal NLP
  • Application of AI in Natural Language Processing
    • Chatbots and Conversational AI
    • Sentiment Analysis and Opinion Mining
    • Translation of Language
    • NLP in Healthcare and Education
  • The AI-Natural Language Processing Duo
  • FAQs about Natural Language Processing in AI

What is Natural Language Processing in AI?

Natural language processing (NLP) is a specialized field of artificial intelligence dedicated to enabling computers to comprehend human speech and writing patterns. This presents a challenge due to the abundance of unstructured data involved. 

NLP in AI

Source: Medium

Human speech and writing styles, often termed 'tone of voice,' are distinctive to individuals. Comprehending context poses a challenge, necessitating semantic analysis for machine learning to grasp it effectively. Natural language understanding (NLU), a subset of NLP, addresses these subtleties through machine reading comprehension, going beyond mere literal interpretations. The overarching goal of both NLP and NLU is to enable computers to comprehend human language sufficiently to engage in natural conversations.

Practical Examples of NLP

1. Voice-activated assistants such as Siri and Alexa.

2. Customer service chatbots employing natural language generation for question answering.

3. Recruitment help on platforms like LinkedIn by analyzing listed skills and experience.

4. NLP-powered tools like Grammarly aid in error correction and simplify complex writing.

5. Language models like Autocomplete are trained to predict subsequent words in a text based on the typed content.

Continuous interactions through writing, speaking, and conversing with computers contribute to their ongoing learning and improvement. An exemplary instance of iterative learning is observed in Google Translate, employing Google Neural Machine Translation (GNMT). GNMT utilizes a vast artificial neural network to enhance fluency and accuracy across languages. Unlike the traditional method of translating one text piece at a time, GNMT aims to translate entire sentences by analyzing millions of examples, leveraging broader context for more relevant translations. It seeks common ground among languages instead of constructing a universal interlingua. Unlike the former process that involved translating from the source language to English before the target language, GNMT employs "zero-shot translate" directly from the source to the target language.

Advancements in Natural Language Processing

Some major advancements in NLP include:

Transformer-Based Models

Transformers have addressed the limitations of traditional recurrent neural networks (RNNs) and convolutional neural networks (CNNs) through the implementation of self-attention algorithms. These transformer-based models have the capacity to simultaneously process complete sequences of textual data, resulting in a more precise and contextually relevant comprehension of human language.

BERT 

Google introduced BERT (Bidirectional Encoder Representations from Transformers) in 2018, a natural language processing model capable of comprehending meaning and context from both the left and right contexts of a word.

This bidirectional method of language interpretation has revolutionized conventional NLP tasks, including query answering, sentiment analysis, and text classification based on meaning and usage.

GPT-3

OpenAI released GPT-3 (Generative Pre-trained Transformer 3) in 2020, boasting as one of the most extensive AI language models with over 1.75 billion parameters intricately integrated into its framework.

Renowned for its ability to generate human-like responses to prompts, GPT-3 has significantly influenced various applications, including content generation, chatbots, and creative writing.

Transfer Learning

NLP models such as GPT-3 and BERT, already pre-trained, can be fine-tuned for specific tasks, demanding less data and training time compared to building them entirely from the ground up. This method of transferring language processing capabilities has democratized NLP, widening its reach to a broader audience, including developers with varying technical proficiency levels.

Multimodal NLP

Conventional NLP primarily revolves around text-based data. However, recent advancements have facilitated language processes to comprehend multimodal data encompassing text, images, and speech.

Multimodal processing holds immense potential across various applications, such as visual question-answering, image captioning, and speech-to-text transcription, marking a significant advancement in this field.

Applications of  AI in Natural Language Processing

Acknowledge the crucial applications of Natural processing language in AI:

Applications of NLP

Source: GeeksforGeeks

Chatbots and Conversational AI

Progress in NLP has led to the development of highly efficient conversational AI systems and chatbots capable of engaging in natural and contextually relevant conversations with real users. These systems find extensive use in virtual home assistants, customer service bots, and tailored support systems.

Sentiment Analysis and Opinion Mining

Leveraging their capacity to understand context and nuances within the text, NLP-based systems can analyze user sentiments and behaviors, conducting expansive opinion mining. This empowers businesses to comprehend customer preferences thoroughly and make data-driven decisions based on valuable insights.

Translation of Language

NLP has significantly enhanced the efficiency and precision of machine translation systems. Neural machine translation frameworks built on transformers have reached exceptional levels of fluency and sophistication in seamlessly translating text across multiple languages, setting new benchmarks in this domain.

NLP in Healthcare and Education

In healthcare, advancements in NLP have resulted in more precise analysis of patient data, records, and research papers. Clinical NLP systems aid in diagnosis, medical coding, and offering personalized treatment recommendations.

Educational tools leveraging NLP can assess and provide tailored feedback and suggestions on students' written assignments, contributing to the enhancement of their language comprehension and writing capabilities. Additionally, virtual language tutors utilizing NLP can engage with learners in a personalized manner to facilitate improved learning experiences.

The AI-Natural Language Processing Duo

Natural language processing, essential for comprehending human communication, stands as a pivotal element for AI to demonstrate its intelligence. Ongoing advancements in deep learning models continually enhance AI's performance in Turing tests, aiming to match human-like abilities. Google's Director of Engineering, Ray Kurzweil, foresees AIs reaching "human levels of intelligence" by 2029.

AI and NLP

Source: Syndell

However, grasping human behavior isn't straightforward, as human expressions may differ from actions. This complexity in understanding human nature poses a challenge. Additionally, the emergence of more intelligent AIs raises the possibility of artificial consciousness, sparking a new realm of philosophical and applied research.

Master NLP in Data Science with IK!

Natural Language Processing has made significant strides, enabling machines to understand, produce, and translate human language with exceptional precision and complexity. Progress in sentiment analysis, text generation, and language translation has unveiled promising opportunities across diverse industries and domains.

As NLP advances, we anticipate additional breakthroughs that will transform communication, data analysis, and decision-making procedures. Embracing NLP across various sectors will foster more streamlined interactions between humans and machines, ushering in a future where language serves as a bridge to innovative solutions and enriched experiences, removing barriers that once existed.

Interview Kickstart is the best platform for you to learn and excel in Natural Language Processing in Data Science. Enroll now to master your skills and beat the interviews like a pro to get hired by top companies. 

FAQs about Natural Language Processing in AI

Q1. Why is NLP required for AI?

NLP is required as it assists in resolving ambiguity in language and provides useful numeric structure to the data for several applications, such as text analytics or speech recognition.

Q2. What is the use of NLP?

By implementing NLP, translators translate languages grammatically, correctly, and more accurately. This is extremely helpful when communicating with someone in another language.

Q3. Does NLP come under AI?

Natural language processing (NLP) is a branch of Artificial Intelligence (AI) that allows computers to generate, manipulate and comprehend human language.

Posted on 
January 12, 2024
AUTHOR

Isha Chakraborty

Senior Content Specialist

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