Natural Language Processing (NLP): What Is It & How Does it Work?
Natural language processing, commonly known as NLP, allows the computer program to understand the human language as it is written and spoken. It is referred to as the natural language, a part of artificial intelligence.
With its long existence of over 50 years, NLP has included an assortment of real-world applications in several fields, including medical research, business intelligence, and search engines. It also has roots in linguistics.
NLP is important because it enables resolving ambiguity in language and adds practical numeric structure to the data for numerous downstream applications, such as text analytics or speech recognition. But did you know how does NLP work and why NLP is so important? Let’s find out!
How does natural language processing work?
NLP is important as it allows computers to comprehend the natural language as humans do. It uses artificial intelligence to handle real-world information, process it, and summarize it so the computer can understand, regardless of whether the language is written or spoken.
Computers use programs to process the information provided to them, and this is converted to code throughout this process to help the computer understand.
Data preprocessing is one of the two main phases in natural language processing, and it includes cleaning and preparing the text data for machines to analyze. This phase highlights features and places the data in a workable form, allowing the algorithm to work with the text provided.
Language processing techniques
Several natural language processing techniques can be done, including:
- Tokenization. – Breaking down text into smaller units.
- Stop word removal. – Removing everyday, mundane words and leaving the program with uncommon words that provide the most information about the text.
- Lemmatization and stemming. – Reducing the words to their primary (root) forms for easier processing.
- Part-of-speech tagging. – Marking the words based on their parts of speech (nouns, adjectives, verbs, etc.)
Once the data has been preprocessed, the second critical phase is algorithm development, which helps the program process the data. There are many different and essential natural language processing algorithms, but two main varieties are typically used:
- Machine learning-based system – This system uses statistics. Machines learn to perform tasks through the training data provided, which helps them adjust their strategies as more data is processed. These systems combine machine and deep learning with neural networks, allowing NLP algorithms to hone their own rules through repeated learning and processing of the information.
- Rules-based system. This system uses carefully developed linguistic rules. This method was used early on in the development of natural language processing and is used today.
Natural Language Processing in Healthcare
When it comes to natural language processing in healthcare, here’s what you need to know. While the way how the NLP can be more than just helpful in healthcare is almost limitless, thus, we’ve provided you with some of the best use cases:
- Improving Clinical Documentation: Instead of manually reviewing EHRs (comprehensive reports of the patient’s overall health), NLP uses formulated data entry and speech-to-text dictation to extract vital data from EHR at the point of care. This enables physicians to focus on providing patients with the essential care they need and guarantees that clinical documentation is precise and kept up to date.
- Accelerating Clinical Trial Matching: Healthcare providers can automatically check enormous quantities of unstructured clinical and patient data and determine qualified candidates for clinical trials using NLP. This helps patients access experimental care that could dramatically improve their condition while supporting innovation in the medical field.
- Supporting Clinical Decisions: The importance of natural language processing is once again shown here as it allows physicians to access health-related information exactly when they need it making it easy, fast, and efficient, and it enables them to make better decisions at the point of care.
NLP use cases in everyday applications
Natural language processing is known to be extremely practical, helpful, and used in many modern real-world applications. Undoubtedly, natural language represents a driving force behind machine intelligence.
One of the most famous NLP examples is Google Translate. Practical machine translation involves more than replacing words in one language with words from another. While not perfect, the core representation of Google Translate is to capture the meaning and the tone of the language being translated and, to the best of its ability, translate the word, phrase, or sentence into another language.
Machine translation tools have made tremendous progress in terms of accuracy from where they began. The best way to test this is to translate a sentence or a phrase into another language and then back again and see whether it will make sense.
While it is not standard, the best spam detection applications use some NLP solutions to scan emails for text classification capabilities. It all works because the natural language processing solution scans the emails for phrases, words that are often used in spam or phishing.
It all may include bad grammar, financial terms, inappropriate urgency, threatening vocabulary, and even misspelled company names, to name a few.
You probably were unaware of virtual agents and chatbots, but virtual assistants like Amazon’s Alexa and Apple’s Siri are powered by AI, meaning NLP. They use speech recognition to identify voice commands and natural language patterns to respond appropriately or with helpful comments.
They also have the option of responding to typed texts. One of the most advanced features that prove just how necessary natural language processing is is the ability to respond to questions in their own words with helpful answers.
A beneficial NLP technique allows the program to summarize large volumes of digital texts and make summaries, enabling people not to have to read the entire test. The best text summarization applications use semantic reasoning and natural language generation (NLG) to add valuable context and conclusions to synopses.
Social media sentiment analysis: This particular NLP feature allows the systems to analyze the language used in social media posts, reviews, even responses and extract emotions and attitudes derived from products, event information, and promotions. This, in turn, helps companies use this feedback and data in product designs or advertising campaigns.
Conclusion of Natural language processing
Natural language processing bridges a crucial gap between software and humans for all businesses. Ensuring and supporting a sound NLP approach is a continuous process, but the results will show across your teams and business.
Businesses use massive amounts of unstructured, text-heavy data and need a way to process it efficiently. A lot of the information that you can find online is stored in databases. It represents natural human language, and until recently, businesses could not analyze this data. For that particular reason, we can conclude that natural language processing is essential.
Natural language processing plays a crucial part in technology and how humans interact with it—being used in many real-world and everyday applications in consumer and business spheres, including cybersecurity, chatbots, search engines, and big data analytics. Even though it is not without its challenges, NLP is anticipated to resume to be an essential part of both industry and everyday life.
Constant improvement and development are crucial, and without the actual users, it will not be able to progress. The AI programs show how much impact natural language processing has on humans as it is made with the thought of making life and business easier for all.