Natural Language Processing or NLP is the branch of AI (Artificial Intelligence) that enables machines to understand, process, and generate human language. NLP aims to bridge the gap between linguistics and computer understanding.
With the help of NLP applications, machines can now process unstructured data from various sources into structured and actionable data points. What would typically take a team of several individuals days or weeks to process can now be accomplished within minutes or hours by computers, due to advancements not only in Machine Learning but also in cheaper storage, parallel processing, fast computing etc.
NLP examples surround us in homes and our workplaces. They have become such a regular feature of our daily lives that we barely notice them. Let us dig deep and look into NLP examples to understand how they came about to influence our lives so much.
The NLP technology has seen evolution from human-computer interaction to human-computer conversation. The term “machine translation” dates back to the 1940s during the second world war. Over the years, the meaning and scope of the term have improved.
NLP can be subdivided into two distinct categories- natural language generation and natural language understanding, which are essential to developing NLP applications. The terms related to language generation and understanding are as follows:
- Phonology- The study that deals with sound and speech patterns.
- Pragmatics- It deals with different uses of language.
- Morphology- The study deals with the structure and systemic relations between words.
- Syntax- It deals with sentence formation and structure.
- Semantics- It deals with the literal meaning of words, phrases and sentences.
Word sense disambiguation and statistics provided a sense of direction to the early research of NLP. This journey was later joined by an array of other essential topics such as information extraction, text summarizing and statistical language processing.
Importance of NLP
NLP has made it possible for computers to analyze voice or text data, understand sentiments and process the information for further use. Machines are unbiased and can analyze language-based data consistently. NLP makes it possible to automate processes such as data interpretation from medical records, social media and many others.
If we consider the staggering amount of unstructured data generated every day, it’s not humanly possible to process it. Human interactions are complex and diverse. There are hundreds of languages and countless dialects, and within each language, there are a unique set of grammatical and syntax rules that needs to be followed. Accents also influence human speech, and each individual has their own unique set of speech patterns and qualities.
NLP offers speech recognition and text analysis to reduce ambiguity in language and provide actionable data.
NLP Examples at Workplaces
NLP applications combine computational linguistics with statistical, deep learning and machine learning models to understand human language. Common NLP examples at workplaces are listed below:
NLP enables the filtering of e-mails and protects your account from spam or phishing activities. It identifies certain information features such as grammatical mistakes, inappropriate texts, nonsensical numbers, etc., to filter them out.
NLP has made information extraction from a large amount of data simple and effective. Recruiters commonly deploy NLP applications to shortlist candidates from resumes. Named entity recognition is used to select candidates with relevant skills and experience for the position.
Chatbots and Smart Assistants
Chatbots and intelligent assistants use text analysis and speech recognition to understand information and respond with the solution. They use deep learning features to understand the need of users better to serve them well.
Text or Speech Translation
With natural language generation and understanding, NLP applications can effectively translate speech or text from one language to another. Effective translation captures the sentiments of the speaker or writer and presents the true picture in another language.
Real-World NLP Examples
Businesses worldwide have realized the potential of NLP and how it can benefit their business. The most common NLP example in use is the chatbot.
Mastercard launched its chatbot feature way back in 2016, compatible with Facebook messenger and functions like a virtual assistant. It provides users with complete information about their spending habits and the available benefits on their cards.
Google has been using the search autocomplete feature in their search engine for years now. Search autocomplete makes it easier for users to find specific information quickly and takes user search history into account.
Gmail uses NLP technology to classify incoming emails into primary, social, and promotional categories.
Innovations in the Industry
To fully capitalize on the human-computer conversation, all aspects of communication must be covered. NLP advancements aim to understand non-verbal cues that are essential for effective communication.
The common uses of biometrics include facial recognition, retina scanner, and fingerprint scanner, which provide an additional layer of security on your computer or smartphone. Unlocking micro-expressions to convey different emotions and feelings could open up a new human-computer interaction and conversation level.
Another critical advancement in NLP and AI is humanoid robotics. Giving computers the ability to think was just the beginning, as it also requires a physical body to interact. Some advanced humanoid robots developed are the Ameca, ASIMO and Sophia.
Businesses are using NLP and AI technology to improve their business functions. NLP applications are advantageous in almost any department. It has also found use in various sectors, such as healthcare, to offer a better customer experience. NLP examples are just proof that validates, AI and NLP technology are here to stay.