Natural Language Processing (NLP) has been in use since the 1950s, when it was first applied in a basic form for machine translation.
But what exactly is natural language processing and why is it important?
In this post, we’ll explore NLP, how it works, types, applications, and more.
What is NLP?
Natural language processing (NLP) refers to the branch of artificial intelligence (AI) focused on helping computers understand and respond to written and spoken language, just like humans.
NLP has a key role in cognitive computing, a type of artificial intelligence that enables computers to collect, analyze, and understand data.
It combines computational linguistics, machine learning, and deep learning in order to process information like humans – the intent and emotion behind spoken or written words.
NLP runs programs that translate from one language to another such as Google Translate, voice-controlled assistants, such as Alexa and Siri, GPS systems, and many others. It is equally important in business operations, simplifying business processes and increasing employee productivity.
Why Is NLP Important?
We collect vast volumes of data every second of every day to the point where processing such vast amounts of unstructured data and deriving valuable insights from it became a challenge.
NLP can process massive volumes of unstructured digital data, such as newspaper articles, reports, emails, forum posts, social media comments, and many others. This helps speed up the process of extracting insights from data and make informed decisions.
How NLP Works
While humans use words and sentences to communicate, computers use numbers. So how does a computer learn to read and understand unstructured data?
In natural language processing, human language is divided into segments and processed one at a time as separate thoughts or ideas. Then it connects them and looks for context between them, which allows it to understand the intent and sentiment of the input.
Natural language processing occurs in two stages: data pre-processing and algorithm development.
Data preprocessing “cleans” the text of redundant words and puts it into a form in which a machine can understand it. This phase includes the following steps:
- Tokenization: Breaking down the text into individual words.
- Stop word removal: Removing redundant words that do not add meaning to the text (removing word cluttering) so that only the words that provide the most information about the text remain.
- Lemmatization/stemming: Reducing the word to the root form to better understand the meaning.
- Part-of-speech tagging: Determining the type of word and classifying it into one of the groups: nouns, pronouns, prepositions, verbs, etc.
After preparation, the data is processed by dedicated NLP algorithms.
The Main Types Of NLP Algorithms
NLP uses many different natural language processing algorithms. The most predominant ones iclude:
One of the earliest approaches to NLP algorithms, the rule-based NLP system is based on strict linguistic rules created by linguistic experts or engineers. NLP follows these rules to categorize the language it is analyzing.
If a rule doesn’t exist, the system won’t be able to understand the and categorize the human language.
This algorithm not only searches for the word you specify, but uses large libraries of rules of human language so the results are more accurate.
Machine Learning Algorithms
Machine learning models are fed examples or training data and learn to perform tasks based on previous data and make predictions on their own, no need to define rules.
When fed training data, machine learning algorithms then make associations between an input and a corresponding output.
What Is NLP Used For?
Natural Language Processing allows the analysis of vast amounts of unstructured data so it can successfully be applied in many sectors such as medicine, finance, judiciary, etc.
NLP is used for multiple functions which can be divided into four categories:
- Text classification is the assignment of labels to texts to classify them. Classification helps recognize emotion in a text’s narrative and discover the intent of the writer or speaker of the text.
- Text extraction involves analyzing and extracting keywords that speak about their essence. Many tools can be used to specify what keywords need to be extracted.
- Machine translation is an automated process by which a computer translates text from one language to another, for example, from Japanese to English.
- Natural language generation is when NLP algorithms collect unstructured data, analyze it, and then use it to create quality content.
Thanks to the mentioned functions, NLP is applied daily in the following:
- Analysis of user experience based on reviews
- Automated customer service performed by voice assistants or chatbots
- Automatic language translation with applications such as Google Translate or Bing Translator
- Text “correction” and plagiarism-checking programs, such as Grammarly or QuillBot AI
- Analysis of large amounts of text, which is particularly useful for academic research purposes or for medical purposes where AI can predict the course of treatment or the outcome of a disease based on previously entered data
- Monitoring the stock market and financial data and facilitating decision-making thanks to analyzing market history and others.
What Are the Advantages of NLP in AI?
Before the development of NLP technology, people communicated with computers using computer languages, i.e., codes. Today, the communication process is simplified. NLP enabled computers to understand human language in written and spoken forms, facilitating interaction.
The advantages of NLP include:
- Reviewing extensive documentation and creating a summary;
- Correcting the accuracy of entered data;
- Powering voice assistants, such as Alexa or Shiri, allowing them to understand voice commands;
- Recognizing sentiment in customer’s comments and messages, improving user experience;
- Utilizing employees’ work by taking over part of the work; for example, chatbots can replace customer service;
- Performing analytics that was previously impossible due to the amount of data.
The Challenges does NLP face?
Despite the advancing technology, NLP comes with its challenges and limitations. The main challenges of NLP are:
Lack of Context
The commands we enter into a computer must be precise and structured and human speech is rarely like that. It is often vague and filled with phrases a computer can’t understand without context.
Natural speech includes slang and various dialects and has context, which challenges NLP algorithms.
Understanding the Tone of Voice
People use figures of speech, such as hyperbole, irony, or sarcasm. Speech recognition microphones can recognize words, but they are not yet advanced enough to understand the tone of voice.
A sentence can change meaning depending on which word is emphasized, and even the same word can have multiple meanings. These are all subtle changes NLP algorithms can miss.
Mistakes in Speech or Writing
Words that are misspelled, pronounced, or used can cause problems in text analysis. A writer can alleviate this problem by using proofreading tools to weed out specific errors but those tools do not understand the intent to be completely error-free.
Language Development and Changes
Language is like a living organism. It’s constantly growing and changing without any rules. Computers “like” to follow instructions, and the unpredictability of natural language changes can quickly make NLP algorithms obsolete.
The Application of NLP in Various Industries
NLP finds application in many areas of life and work. Here are some of the sectors where it is indispensable:
AI in healthcare is based on NLP and machine learning as the most important technologies. NLP enables the analysis of vast amounts of data, so-called data mining, which summarizes medical information and helps make objective decisions that benefit everyone.
The medical staff receives structured information about the patient’s medical history, based on which they can provide a better treatment program and care.
NLP in marketing is used to analyze the posts and comments of the audience to understand their needs and sentiment toward the brand, based on which marketers can develop further tactics.
In addition to sentiment analysis, NLP is also used for targeting keywords in advertising campaigns. It also empowers chatbots that solve user queries and contribute to a better user experience.
NLP is used in banking, the stock market, and all other financial sectors. In addition to processing financial data and facilitating decision-making, NLP structures unstructured data detect anomalies and potential fraud, monitor marketing sentiment toward the brand, etc.
The advantage of NLP in this field is also reflected in fast data processing, which gives analysts a competitive advantage in performing important tasks.
Since the Covid pandemic, e-learning platforms have been used more than ever. NLP evaluates student knowledge and monitors progress. The evaluation process aims to provide helpful information about the student’s problematic areas, which they should overcome to reach their full potential.
The obtained results are useful both for the students, who do not waste time but concentrate on the areas in which they need to improve and for the teachers, who can adjust the lesson plan to help the students.
Wrapping Up on Natural Language Processing
Natural Language Processing helps computers understand written and spoken language and respond to it. The main types of NLP algorithms are rule-based and machine learning algorithms.
NLP is used for:
- Text classification
- Text extraction
- Machine translation
- Natural language generation
While there are numerous advantages of NLP, it still has limitations such as lack of context, understanding the tone of voice, mistakes in speech and writing, and language development and changes.
NLP is already prevalent in everyday life and chances are you use it daily. You’ve probably translated text with Google Translate or used Siri on your iPhone. Both services work thanks to NLP machine translation or speech recognition.
NLP has already changed how humans interact with computers and it will continue to do so in the future.