Introduction to Natural Language Processing. What Is NLP?
Last updated on September 25th, 2023
February 14th 2023
This text was fully written by humans.
SUMMARY / KEY TAKEAWAYS
Natural Language Processing (or, NLP) is a branch of Artificial Intelligence that deals with the interaction between humans and computers by using “natural language:” models and algorithms that can be understood by humans and generate human-like language.
The primary goal of natural language processing is to make it possible for computers to process and understand human language in the same way a real human would.
In this article, we will guide you through the basics of Natural Language Processing: concepts, tools, and positions associated with NLP that you might consider as a future career. We will also discuss the scope of its current applications.
Table of Contents
- What Is Natural Language Processing?
- Text Preprocessing: A Vital Part of NLP
- Text Representation
- Named Entity Recognition (NER)
- Tools Used by Natural Language Processing Engineers
- The Main Applications of Natural Language Processing
- Jobs and Designation For Natural Language Processing Engineers
- Conclusion: What is the Future of Natural Language Processing?
What Is Natural Language Processing?
In the past few months, we have all heard of chatbots such as ChatGPT or text-to-image software such as DALL-E. In fact, so many new open AI tools flooded the market that many professionals and businesses wonder: how do I even navigate myself in this new reality? Is it the do-or-die for me?
Natural Language Processing (or, NLP) is a branch of Artificial Intelligence that deals with the interaction between humans and computers by using “natural language:” models and algorithms that can be understood by humans and generate human-like language.
The primary goal of natural language processing is to make it possible for computers to process and understand human language in the same way a real human would.
NLP has been here for several decades and has evolved successfully over time. In the early days, Natural Language Processing was mainly focused on language translation and rule-based systems. However, with improvement and advancement in machine learning and deep learning methods and techniques, Natural Language Processing has become much more sophisticated and capable of handling a wider range of language-related tasks.
But how does NLP even work? In this article, we will guide you through the basics of Natural Language Processing: concepts, tools, and positions associated with NLP that you might consider as a future career. We will also discuss the scope of its current applications.
Firstly let’s talk about the NLP analysis in itself. How is it done?
1. Text Preprocessing: A Vital Part of NLP.
The vital step in NLP is text preprocessing which includes sorting and transforming the raw text data into a format according to machine learning algorithms.
The main steps in text preprocessing are:
Tokenization.
Breaking down a sentence into its constituent words or tokens is called tokenization. It is an important step in Natural Language Processing as it helps identify the text’s structure and meaning.
Stemming and Lemmatization.
Stemming and lemmatization are methods used to reduce words to their core form. Stemming involves removing the suffixes from words, while lemmatization involves finding the root word for a given the word. Both of these techniques help reduce the data size and improve the analysis accuracy.
2. Text Representation.
Once the text has been preprocessed, it needs to be represented in a format that machine learning algorithms can understand and further work with. There are several main techniques for text representation:
N-grams.
N-grams are sequences of N words that appear together in a document. N-grams can only represent text data in a way that considers the connection between subsequent words.
Term Frequency-Inverse Document Frequency (TF-IDF).
TF-IDF is a form of representation of text data that considers both the frequency of a word in a document and the number of documents that contain the word. This representation is often used in text classification and sentiment analysis.
Supervised Learning.
A machine learning technique that trains the algorithms on a labeled dataset is known as supervised learning. This approach is used for text classification, where the labels are known in advance.
Unsupervised Learning.
Unsupervised learning is a machine learning technique slightly different from supervised learning. It trains algorithms on an unlabeled dataset (which more resembles the “real-world case”). This approach is used for text classification where the labels are not known in advance.
Semi-supervised Learning.
Semi-supervised learning is a joint machine learning technique that combines both supervised and unsupervised learning. This approach is used for text classification where some labels are known and some are not.
3. Named Entity Recognition (NER).
Furthermore, the Named Entity Recognition (NER) identifies named entities in a text, such as people, organizations, locations, and dates.
There are two main types of named entities:
NER techniques.
There are several techniques for named entity recognition, including rule-based systems, dictionary-based systems, and machine learning-based systems. Rule-based systems use a set of rules to identify named entities, while dictionary-based systems use a pre-defined list of named entities.
On the other hand, machine learning-based systems use algorithms such as decision trees, support vector machines, and neural networks to identify named entities.
Part-of-Speech (POS) Tagging.
Part-of-speech (POS) tagging identifies the part of speech for each word in a text. An important step in NLP as it helps identify the text’s structure and meaning. There are multiple methods for POS tagging, including rule-based systems, dictionary-based systems, and machine learning-based systems.
Sentiment Analysis.
Sentiment analysis is the process of determining the sentiment expressed in a text. An essential application of NLP as it can be used to analyze the opinions and emotions of people in a given text. Dictionary-based systems, rule-based systems, and machine learning-based systems are several techniques for sentiment analysis.
Tools Used by Natural Language Processing Engineers.
There are several tools available for Natural Language Processing language engineers to use in their work. Here, we will list some of the most popular open-source NLP tools used by Natural Language Processing engineers that can help you start a career as an NLP engineer:
Natural Language Toolkit (NLTK).
The Natural Language Toolkit (NLTK) is a popular open-source NLP library for Python. It provides a wide range of text processing and analysis functionalities, including tokenization, stop-word removal, and sentiment analysis.
NLTK also has a large community of users and developers, making it a great choice for NLP engineers looking for a tool with a lot of support and resources.
SpaCy.
SpaCy is yet another popular Natural Language Processing library for Python. It provides fast and accurate text processing and analysis, making it a great choice for NLP engineers working on real-time applications. spaCy is also known for its excellent documentation and community support, making it a great option for NLP engineers of all skill levels.
Stanford Natural Language Processing.
The Stanford NLP library is popular for Java and other programming languages. It provides a wide range of text processing and analysis functionalities, including tokenization, stopword removal, and sentiment analysis. Stanford Natural Language Processing is a great choice for NLP engineers looking for a tool that is fast, accurate, and easy to use.
Gensim.
Gensim is a popular NLP library for Python. It provides a wide range of text processing and analysis functionalities, including topic modeling, word embedding, and text classification. Gensim is a great choice for NLP engineers working on large-scale NLP projects as it is highly scalable and efficient.
OpenAI GPT.
OpenAI GPT is a popular Natural Language Processing model developed by OpenAI. It is a transformer-based language model that can generate human-like text. OpenAI GPT is a great choice for NLP engineers looking to build NLP applications that require text generation capabilities.
Transformers are powerful attention-based mechanisms first introduced in a paper entitled “Attention Is All You Need” by Vaswani et al. in 2017. Please learn more about transformers from our interview with Krzysztof Choromański.
BERT.
BERT is yet another transformer-based language model developed by Google. It is a popular Natural Language Processing model for sentiment analysis, text classification, and named entity recognition tasks. BERT is a great choice for engineers looking to build NLP applications that require high accuracy and robust performance.
The Main Applications of Natural Language Processing.
NLP has many applications across various industries, including chatbots and virtual assistants, text summarization, machine translation, question answering, text generation, speech recognition, and sentiment analysis in customer service.
Machine Translation.
Machine translation basically means automatically translating text from one language to another. It was one of the first applications of NLP. Who didn’t use Google Translate 10 or even 15 years ago?
Today, machine translation remains a popular application of NLP in e-commerce, travel, and international communication; with the advancement of NLP techniques, machine translation has become more accurate and capable of handling long-complex texts.
In fact, most of the professional translators today focus on editing text translated with the use of machine translations tools rather than translating the whole text from scratch.
Chatbots and Virtual Assistants.
Chatbots and virtual assistants are popular applications of Natural Language Processing. They use NLP techniques to understand and respond to user input, making it possible to automate customer service and support tasks.
Text Summarization.
Text summarization is the process of decreasing the length of a text (or simply, making it short and concise) while mentioning its important information. It is a famous application of NLP among professionals and organizations and is often used in journalism for news aggregation, email and document summarization.
Text Generation.
The concept of automatically writing text using NLP methods is known as text generation. Natural language processing is mainly used in content writing, fictional writing, and cold-email marketing.
Speech Recognition.
The method of changing audible words into written is known as speech recognition. It is a common natural language processing procedure that is found in different spaces, such as voice-activated gadgets, call centers, and accessibility technologies. NLP methods are used to convert human-spoken words into text and evaluate the meaning of the speech.
Sentiment Analysis in Customer Service.
Sentiment analysis is the method of identifying whether a text’s tone is good, negative, or neutral. It’s a common usage of NLP in customer support, where it’s used to read and respond to consumer comments. Customer feedback is reviewed using NLP algorithms to identify customer satisfaction and find areas for progress.
Jobs and Designation For Natural Language Processing Engineers.
NLP is a fast-expanding industry with several fascinating work possibilities for anyone interested in the area. NLP engineers, research scientists, data analysts, and consultants are in great demand as businesses discover the value of NLP.
NLP Engineer.
An NLP engineer is in charge of creating and applying NLP strategies and frameworks for a number of applications. They focus on problems such as text categorization, sentiment analysis, and named entity recognition. To create NLP solutions, they combine NLP tools and computer languages such as Python and R.
Current expected salary according to glassdoor: $136,884.
Research Scientist in Natural Language Processing.
A research scientist in NLP is in charge of studying NLP procedures and creating new programs and designs. They work at both academic and research institutions, as well as in industry. They build NLP solutions and publish research papers using NLP technologies and programming languages such as Python and R.
Current expected salary according to glassdoor: $158,404
Natural Language Processing Data Analyst.
NLP data analysts are in charge of collecting and analyzing NLP data. They focus on problems such as opinion analysis, text categories, and named entity verification. They study NLP data and make informed conclusions using NLP tools and programming languages such as Python and R.
Current expected salary according to glassdoor: $147,095
Natural Language Processing Consultant.
An NLP consultant is a person who gives expert analysis and direction on NLP solutions and applications. They work with companies to assist them in discovering the power of NLP and how to follow it to meet their goals. They design NLP solutions and give professional advice using NLP technologies and programming languages such as Python and R.
Current expected salary according to glassdoor: $110,592
Natural Language Processing in Healthcare.
NLP is utilized in a variety of settings in healthcare, including hospital records monitoring, patient treatment, and clinical decision-making. In healthcare industries, NLP engineers and data analysts provide NLP solutions for tasks such as sentiment analysis, text classification, and named entity verification.
Natural Language Processing in Finance.
Various fields of finance perform NLP, including financial analysis, risk management, and spam prevention. In financial firms, NLP engineers and data analysts provide NLP solutions for tasks such as ideology analysis, text presentation, and named entity verification.
Natural Language Processing in Marketing.
Customer feedback analysis, product suggestions, and sentiment analysis are all examples of how NLP is utilized in marketing. In marketing firms, NLP developers and data analysts provide NLP solutions for tasks such as sentiment analysis, text categorization, and named entity identification.
Conclusion: What is the Future of Natural Language Processing?
The future potential of the Natural Language Processing Tools is huge. NLP algorithms will change the ways in which we communicate, allow us to communicate with machines without any programming skills. This can lead to many breakthroughs in the entertainment industry, education, and the way we work in general.
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Please cite as:
Siddiqui, H. A., Bielczyk, N. (2023, February 14th). Introduction to Natural Language Processing. What Is NLP? Retrieved from https://ontologyofvalue.com/introduction-to-natural-language-processing-nlp/
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