Natural Language Processing for Beginners: Applications, Challenges, and Key Concepts
NLP(Part-1)
📚Chapter 1: Introduction
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Introduction
In a world where communication reigns supreme, the ability to understand and interact with human language is invaluable. Natural Language Processing (NLP) is the field of artificial intelligence (AI) dedicated to making this possible. From powering virtual assistants to analyzing vast amounts of text data, NLP plays a pivotal role in shaping the way we interact with technology and each other.
Sections
What is Natural Language Processing (NLP)
Why NLP is so important?
Brief History of NLP
What are the Main Challenges in NLP Tasks
Conclusion
Section 1- What is Natural Language Processing (NLP)
NLP is primarily about developing systems that allow machines to communicate with humans in their natural language. It encompasses two key areas:
Natural Language Understanding (NLU): The goal here is to make machines comprehend and interpret human language. NLU allows systems to recognize the intent behind the text or speech, extracting key information such as emotions, entities, and actions. For instance, when you ask a voice assistant “What’s the weather like?”, NLU helps the system determine that the user is asking for weather information.
Natural Language Generation (NLG): Once a machine understands human input, NLG takes over by generating appropriate responses. An example of this is AI writing assistants that can craft sentences or paragraphs based on the data provided.
Def 1: Natural language refers to the medium in which humans communicate with each other. This could be in the form of writing (text) for example emails, articles, news, blogs, bank documents, etc, or speech for example lectures, speeches, audio calls, etc. NLP is one of the major AI technologies aimed at making machines capable enough to interpret speech and text-based human language.
Def2: Natural Language Processing is a branch of linguistics, AI, and CS for the manipulation, and translation of natural language which gives machines the ability to read, understand and derive meaning from human language [4].
Def 3: Simply put, NLP is a set of computational techniques that allow machines to understand and manipulate human spoken languages
Section 2- Why NLP is so important?
By now we have to work with a huge amount of data. In Machine Learning we mainly work with those numerical values. How we can get some actions on text data like news reports, social media comments and posts, and customer reviews in the online stores? We can use Natural language processing techniques to do that.[3].Not only that, Even now we have made daily work easier by using applications made from NLP. Summarization applications, spell checker applications, and machine translations are some of them [3].
Computers and machines are great at working with tabular data or spreadsheets. However, human beings generally communicate in words and sentences, not in the form of tables. Much information that humans speak or write is unstructured. So it is not very clear for computers to interpret such. In natural language processing (NLP), the goal is to make computers understand the unstructured text and retrieve meaningful pieces of information from it [2]
“Well NLP is cool and stuff, but how can we leverage it to improve our businesses more efficiently? How it could differ from the more traditional techniques?” [5].As we have said before, NLP allows machines to effectively understand and manipulate human languages. With that, you will be able to automate a lot of tasks and improve their rapidity and scale, like data labeling, translation, customer feedback, and text analysis. Applying NLP to real-world cases and not just for research purposes, will bring a significant competitive advantage to many businesses [5].
Section 3- Brief History of NLP
Natural Language Processing (NLP) boasts a diverse history that stretches across multiple decades. The domain of NLP has undergone substantial evolution, starting from its inception in the 1950s to the present-day advanced models capable of comprehending and producing language akin to humans.
Early Years (1950s-1960s)
The Dartmouth Summer Research Project on Artificial Intelligence, spearheaded by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, is frequently cited as the origin of Artificial Intelligence (AI) and Natural Language Processing (NLP). During the 1950s and 1960s, pioneers such as Alan Turing, Noam Chomsky, and Marvin Minsky established the groundwork for NLP through their investigations into machine learning, linguistics, and computer science.
Rule-Based Approach (1970s-1980s)
During the 1970s and 1980s, research in Natural Language Processing (NLP) was centered on rule-based methods for language processing. This required the formulation of hand-coded rules for the analysis and generation of language. While this method achieved some level of success, it was ultimately constrained by its lack of capacity to manage the intricacies and variations of human language.
Statistical Approach (1990s-2000s)
During the 1990s and 2000s, there was a transition to statistical methods in natural language processing (NLP). Researchers started to employ machine learning algorithms and statistical models for language analysis and generation. These methods proved to be more efficient than rule-based systems, although they also had drawbacks, such as the need for extensive annotated datasets.
Deep Learning Era (2010s-present)
The 2010s heralded the advent of the deep learning revolution in natural language processing (NLP). The emergence of vast datasets, significant improvements in computational power, and innovative algorithms such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformers have facilitated the development of highly sophisticated and accurate NLP models.
Section 4- What are the Main Challenges in NLP Tasks?
Teaching machines how to understand human language is no walk in the park. There are a bunch of tough hurdles we still haven’t quite figured out yet, such as [7]
1- Ambiguity and Polysemy: Basically, human language can be confusing because words can mean different things depending on the situation. Figuring out what someone really means can be tough in NLP.
2-Variability and Diversity:Languages are all over the place, and everyone has their own way of saying things. It can be tough to keep up with all the different ways people talk and understand the different styles.
3- Context Understanding:It’s super important to get the context right if you want to understand language. NLP systems have to grasp not just what’s being said right now, but also the bigger picture of a conversation or text.
4-Lack of Common Sense Understanding:People use a lot of common knowledge and common sense when they communicate, but machines might need some help getting better at it. It’s a tough job to teach machines how to use common sense in Natural Language Processing.
5-Sarcasm and Irony: Figuring out sarcasm and irony can be tough for NLP models because there’s usually a disconnect between what’s actually said and what’s meant.
Conclusion
As we continue to push the boundaries of what is possible with technology, NLP stands out as a shining example of innovation and progress. By bridging the gap between humans and machines, NLP has the potential to revolutionize how we communicate, learn, and interact in the digital age.
In the coming years, we can expect to see even greater advancements in NLP, unlocking new possibilities and reshaping the way we experience the world around us. Whether it’s powering the next generation of intelligent assistants or revolutionizing how we analyze and understand language, the future of NLP is bright and full of promise.
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To Do List
1- Collects Keys points from the blogs
Source
1- Natural Language Processing with Classification and Vector Spaces
2- Natural Language Processing (NLP) with Python
4- Day 1–30 Days of Natural Language Processing Series with Projects
5- Building An NLP Project From Zero To Hero (1): Project Overview
6- What is Natural Language Processing (NLP), and Why is it Even Relevant in 2022?