The term “Natural Language Processing” (NLP) refers to the ability of computers to dissect and understand human language.
It lies at the heart of the technologies that we employ on a daily basis, from translation programs, chatbots, spam filters, and search engines to grammar correction programs, voice assistants, and tools for monitoring social media platforms.
Learn the fundamentals of NLP, its limitations, and the most widely used NLP applications in the business world.
You’ll see firsthand how simple it is to implement an NLP workflow without writing a single line of code.
What Is Natural Language Processing (NLP)?
Natural Language Processing, also known as NLP, is a subfield of Artificial Intelligence (AI) that aims to make it possible for machines to understand human language.
Using the power of both linguistics and computer science, natural language processing (NLP) studies the rules and structure of language in order to develop intelligent systems (which are run on machine learning and NLP algorithms) that are capable of comprehending, analyzing, and deriving meaning from both written and spoken text and speech.
What Is NLP Used For?
Natural language processing (NLP) examines facets of language such as syntax, semantics, pragmatics, and morphology in order to decipher their role in constructing and conveying meaning in spoken and written communication.
Then, the field of computer science takes this linguistic understanding and converts it into machine learning algorithms that are rule-based and can do individualized jobs.
Consider the ubiquitous Gmail. Using a natural language processing (NLP) job called keyword extraction, inbound emails are automatically sorted into Promotions, Social, Primary, and Spam folders.
Machines can learn to classify incoming emails into the correct inbox by “reading” the subject lines and “learning” to link key phrases with appropriate labels.
NLP Benefits And Its Features
There are many ways in which NLP can help your business succeed; here are just a few high-level advantages:
Perform large-scale analysis: Machines can use Natural Language Processing to automatically comprehend and analyze vast swaths of unstructured text data, such as social media comments, customer service tickets, online reviews, news articles, and more.
Automate processes in real-time: With the aid of natural language processing tools, machines can be taught to organize and route data with minimal human oversight, all while working around the clock.
Tailor NLP tools to your industry: Algorithms used in natural language processing can be customized to meet your requirements, including the ability to understand sarcasm and improper use of language.
How Does Natural Language Processing Work?
To train machines to make associations between an input and its related output (tags), NLP technologies use text vectorization to convert human-readable material into a form that computers can comprehend.
In order to predict new data (unseen texts), machines utilize statistical analysis to compile their own “knowledge bank” and determine which qualities best reflect the texts.
One major benefit of machine learning models is that they may train themselves without any human intervention or rule definitions. All you need is some appropriate training data, preferably with multiple instances of the tags you’re interested in studying.
You may achieve extremely granular results from natural language processing tasks like sentiment analysis, keyword extraction, topic categorization, intent detection, and more when you use cutting-edge deep learning algorithms to chain these processes together.
Common Tutorial for Natural Language Processing
Many NLP tasks use syntactic and semantic analysis to break up human language into chunks that a computer can understand.
Using a graphical representation called a parse tree, syntactic analysis (also known as parsing or syntax analysis) determines a text’s syntactic structure and the dependency links between words.
The goal of semantic analysis is to determine the significance of a given word or phrase. Semantics is one of the trickiest parts of natural language processing since language is polysemic and confusing.
Semantic tasks are used to figure out what words mean and what a piece of writing is about by looking at how sentences are put together, how words interact with each other, and other related ideas.
Some of the most important components of semantic and syntactic analysis are outlined below:
In natural language processing, tokenization is a crucial step for separating a text string into individual words with the same meaning.
One can tokenize a text into sentences and words, or do sentence tokenization and word tokenization independently. Word tokens are typically separated by spaces, while sentence tokens are typically separated by full stops.
However, high-level tokenization can be used for more complicated structures, such as collocations (words that are frequently used together) (e.g., New York).
An illustration of the text-simplifying power of word tokenization Here’s an example of the text simplification made possible by word tokenization:
Customer service couldn’t be better! = “customer service” “could” “not” “be” “better”.
Tokens in a text can be annotated with information about their grammatical function through a process called part-of-speech tagging (or PoS tagging). Words like “verb,” “adjective,” “noun,” “pronoun,” “conjunction,” and “intersection” are all examples of common PoS tags. This is how the preceding example would be formatted in this scenario:
“Customer service”: NOUN, “could”: VERB, “not”: ADVERB, be”: VERB, “better”: ADJECTIVE, “!”: PUNCTUATION
PoS tagging can help you understand the meaning of phrases by highlighting connections between words.
The term “dependency grammar” describes the relationships between words in a sentence. To deduce the syntactic structure of a sentence, a dependency parser investigates the connections and modifications between ‘headwords.’
Constituency Parsing is a grammatical process that identifies the phrases in a sentence in order to create a visual representation of the sentence’s syntactic structure.
The process involves making use of abstract terminal and non-terminal nodes connected to words. The complexity of the text you wish to analyze and the parsing algorithm(s) you choose should both be carefully considered.
Lemmatization & Stemming
In most cases, we choose an inflected form of a word when using it in speech or writing. Naturally, Language Processing (NLP) uses lemmatization and stemming to convert these words back to their grammatical stems.
The lemma refers to the dictionary definition of a word. The tenses “is,” “are,” “am,” “were,” and “be” are all classified as lemmas of the verb “be.” Accordingly, “African elephants have four nails on their front feet” becomes “elephants have four nails” after lemmatization.
African elephants have four nails on their front feet = “African,” “elephant,” “have,” “4”, “nail,” “on,” “their,” “foot”]
You can see how lemmatization modifies the sentence in this example by comparing it to its original form.
The stem of a word is the word’s etymological root. Words are “trimmed” when stemming, so the resulting stems may not always be accurate in terms of meaning.
The stem “consult” is at the base of many related words, including “consult,” “consultant,” “consulting,” and “consultants.”
Lemmatization, which uses a dictionary and selects the most relevant lemma based on context, contrasts with stemming, which only works on individual words. Take this sentence as an illustration:
“This is better”
Lemmatization changes “better” to “good,” whereas stemming leaves it untouched. Though stemmers often produce less precise outcomes than lemmatizers, they are simpler to implement and run more quickly.
However, lemmatizers are highly suggested for those looking for more specific linguistic guidelines.
NLP text processing cannot proceed without first removing stop words. The process entails eliminating common but meaningless terms from a sentence, such as “which,” “to,” “at,” “for,” “is,” and so on. The lists of stopwords can be tailored to contain the words you wish to filter out.
So, you want to categorize support tickets according to their subjects. Removing the “hello,” “I,” “am,” and “with” from “Hello, I’m having difficulties logging in with my new password” could help you focus on the most pertinent phrases, such as “trouble,” “logging,” “new,” and “password,” which would give you a better idea of the issue being reported.
Word Sense Disambiguation
Words can mean different things depending on the setting in which they are used. Just consider the word “book” as an illustration:
- It’s a fantastic novel, and you should definitely pick it up.
- You need to book the flights immediately.
- You need to complete year-end accounting procedures.
- If you want to avoid any problems, you should follow the rules.
Word sense disambiguation (WSD) can be accomplished using one of two primary methods: a knowledge-based (or dictionary approach) method, or a supervised method.
The former makes use of dictionary definitions of unclear words in a text to infer meaning, while the latter is founded on natural language processing algorithms that acquire knowledge through experience.
Named Entity Recognition (NER)
The process of extracting entities from a text, known as named entity recognition, is one of the most well-liked tasks in semantic analysis. An entity could be anything from a person’s name to a physical location, a business, an email address, or any combination of these.
Further, NLP has a sub-task called relationship extraction, which identifies connections between three words. In the sentence “Susan lives in Los Angeles,” Susan, a person, is linked to Los Angeles, a location, via the semantic category “lives in.”
Text classification is the process of understanding the meaning of unstructured text and organizing it into predefined categories (tags).
One of the most popular text classification tasks is sentiment analysis, which aims to categorize unstructured data by sentiment. Other classification tasks include intent detection, topic modeling, and language detection.
Challenges of Natural Language Processing
The inherent ambiguity of the human language is one of the many obstacles that make natural language processing challenging. Humans have a hard enough time correctly analyzing and categorizing human language as it is.
The use of sarcasm is a good illustration. Is there a way to train a computer to recognize a statement that means the exact opposite of what it says?
It would be difficult to train a machine to recognize the sarcasm in the comment below, despite the fact that humans could do it with relative ease.
“I’d be broke if I had a dollar for every witty thing you say.”
Data scientists must train NLP tools to comprehend context, word ambiguities, and other complex concepts related to messages if they are to fully understand human language.
However, when refining NLP models, it’s important to take into account factors like culture, background, and gender as well. For instance, sarcasm and other forms of humor might have vastly different cultural contexts in other countries.
The disarray of human speech is being tamed by advances in natural language processing and robust machine learning algorithms (typically numerous employed in partnership), which are bringing order to nuances such as sarcasm.
New developments in natural language processing indicate that this field is poised to dramatically alter the ways in which people and machines work together in the not-too-distant future.
Natural Language Processing Examples
Even though natural language processing is still in its early stages, it is already being put to many practical uses. When you encounter NLP, chances are you won’t even know it.
Most of the time, NLP is quietly working in the background of the apps and programs we use every day to make our lives easier and better. We’ve outlined some of the most prevalent and potent applications of NLP in the real world below:
Top 10 Common Examples of NLP
- Email filters
- Virtual assistants, voice assistants, or smart speakers
- Online search engines
- Text prediction and autocorrection
- Track brand sentiment on social media.
- Sorting through customer fees
- Automating processes in customer support
- Machine translation
- Natural language generation
Natural Language Processing Tips
Cloud-based services like MonkeyLearn provide pre-built NLP analysis templates for common data formats. Below, we’ll walk you through a tutorial on how to use our bespoke template for sentiment analysis and keyword extraction
- Choose a template for Keyword + Sentiment Analysis
- Put your text files online. (Use our sample dataset if you don’t have a CSV.)
- Match the CSV columns to the fields on the dashboard.
- Give your process a name
- Wait for your information to come in.
- Look at the dashboard!
Final Words on Natural Language Processing
Training computers to understand human language and carry out routine linguistic tasks automatically, such as translation, summarization, classification, and extraction, is a key component of natural language processing, which is revolutionizing the way linguistic data is analyzed and interacted with.
Not that long ago, it was unthinkable that computers would ever be able to comprehend spoken language.
Although natural language processing (NLP) has made significant progress in a short amount of time thanks to developments in linguistics, computer science, and machine learning, it is still a relatively new area of study within the larger field of artificial intelligence. This is despite the fact that these fields have made significant strides in recent years.
In recent years, natural language processing (NLP) has become more accessible because of these technological advancements.
Plug-and-play software that is based on NLP, such as MonkeyLearn, is making it easier for organizations to construct one-of-a-kind solutions that automate procedures and boost customer understanding.
Frequently Asked Questions (FAQs)
What are Natural language Processing applications?
Check out these examples of NLP Applications:
1) Email filters
2) Smart assistants
3) Search results
4) Predictive text
5) Language translation
6) Digital phone calls
7) Data analysis
8) Text analytics
What is Natural Language Processing with Python?
NLP, or natural language processing, is the study of converting human speech into a format that computers can understand. Natural Language Toolkit (aka NLTK) is a Python module useful for natural language processing. A large portion of the information at your disposal is unstructured and can be read by humans.
What Is Natural language processing in machine learning?
The field of computer science and, more specifically, the subfield of artificial intelligence (AI) known as natural language processing (NLP) focuses on teaching computers to understand written and spoken language in a manner similar to that of humans.
What is natural language processing with example?
One area of AI is called Natural Language Processing (NLP) (AI). It facilitates the processing of human language by machines, allowing for the automation of routine operations. Machines have been used for various tasks such as translation, summarization, classification of tickets, and spell check.
What is natural language processing technique?
The Processing of Natural Language (NLP) The goal of natural language processing is to create machines that can understand and respond to text or voice input, and even carry on conversations, in ways that are strikingly similar to how people do it.
Is Alexa an example of NLP?
Microsoft Program Manager Adi Agashe claims that Alexa is based on natural language processing (NLP), a method for translating spoken language into text. Amazon listens in on everything you say.
What is NLP and its benefits?
Researchers can better organise unstructured data for better patient care, research, and illness diagnosis with the use of natural language processing technologies. On this date in 2022: Researchers and doctors require access to extensive patient data and medical literature to provide excellent care and improve patient outcomes.
Is NLP an algorithm?
Most natural language processing (NLP) algorithms have their roots in machine learning. NLP can use machine learning to automatically learn these principles by examining a group of instances (i.e. a big corpus, like a book, down to a collection of sentences) and drawing a statistical conclusion, eliminating the need for extensive hand-coding.
What is the main task of NLP?
Automatic summarization, discourse analysis, machine translation, conference resolution, speech recognition, etc. are just a few of the many important tasks that NLP is used for. Automatic summarising allows the computer to generate a summary of a given text, article, journal, etc.
What is the biggest challenge in NLP?
NLP has been expanding, and NLU (Natural Language Understanding) has been helping computers understand and respond to human language, but the biggest obstacle is the fluidity and inconsistency of the language itself.
What is the best NLP model?
Words may have statistical dependencies on one another, and GPT-3 can handle it. It has been trained on 45 TB of text and 175 billion parameters collected from around the internet. It is among the best pre-trained NLP models available.
Which industry uses NLP the most?
In order to better understand their clientele and cater to their needs, businesses are increasingly turning to natural language processing (NLP) methods. Also, thanks to NLP, businesses may more easily learn about customers’ difficulties.
Can you make money with NLP?
The salaries of NLP Practitioners, who sometimes double as NLP Coaches, fall anywhere from the low end (Rs 3,000) to the high end (Rs 50,000). As a result of the additional skillsets they bring to the table, NLP Coaches typically command higher rates of pay than NLP Practitioners who focus just on Personal Change Work.
What Is Natural language processing in AI?
The salaries of NLP Practitioners, who sometimes double as NLP Coaches, fall anywhere from the low end (Rs 3,000) to the high end (Rs 50,000). As a result of the additional skillsets they bring to the table, NLP Coaches typically command higher rates of pay than NLP Practitioners who focus just on Personal Change Work. c