Overview
Have you ever thought about how virtual assistants like Siri or Alexa understand what you say or even if you are not able to explain the exact scenario these assistants understand your intent and provide you most relatable answer.
Well, it’s all thanks to Natural Language Processing AI that allows computers to understand and manipulate human language revolutionizing the way we interact with machines, paving the way for intuitive and natural user interfaces.
In this article, we will be discussing natural language processing techniques, How NLP works, and some applications of NLP.
So let’s get started.
What is NLP
Natural language processing NLP is a subfield of artificial intelligence focused on enabling computers to understand and process human language. This encompasses understanding the meaning of text, the intent behind spoken words, and the nuances of human communication.
The primary goal of NLP language is to enable computers to understand, interpret, and generate human-like text, making it a bridge between human communication and machine understanding.
As a growing technology, it is predicted that the natural language processing (NLP) market size will grow from USD 15.7 billion in 2022 to USD 49.4 billion in 2027 with a Compound Annual Growth Rate (CAGR) of 25.7%
How NLP works
NLP AI empowers computers to understand human language just as we do. It doesn’t matter if the language is spoken or written— NLP AI takes in real-world input, processes it, and interprets it in a way that computers can understand. Similar to how humans have various sensors like ears for hearing and eyes for seeing, computers use programs and microphones for input. Just as humans use their brains, computers employ programs to process information. During the processing, the input eventually gets translated into code that the computer can understand.
NLP techniques involve a combination of linguistics, computer science, and AI algorithms to process and analyze human language.
Here’s How NLP works
Tokenization
This step involves breaking down text into smaller units, such as words or phrases.
Part-of-Speech Tagging
This Identifies the parts of speech for each word or phrase such as nouns, verbs, adjectives, etc.
Named Entity Recognition (NER)
This is when the systems Identify and classify entities (e.g., names, locations) in the text.
Parsing
This involves breaking a sentence down into its grammatical components, such as subject, verb, object, etc.
Sentiment Analysis
This involves understanding the emotional tone and meaning of words expressed in the text.
Machine Translation
This involves translating text from one language to another.
Natural language processing applications
Natural language processing AI has found applications in various domains, revolutionizing the way we interact with technology.
Some notable NLP applications include:
Chatbots and Virtual Assistants
Chatbots and virtual assistants leverage NLP techniques to engage in natural language conversations with users. They are designed to understand natural language and provide fitting responses to user’s queries using natural language generation, offering assistance in various tasks, from answering questions to completing transactions. Your company can save time and money by automating repetitive tasks with chatbots that are enabled with natural language processing (NLP).
According to Chatbot Magazine, using NLP-enabled predefined tasks could save the car industry up to 30% on customer support costs.
Language Translation
NLP algorithms in plays a crucial role in language translation applications, allowing users to translate text or speech from one language to another. This technology facilitates cross-language communication and breaks down language barriers in diverse global contexts.
In the business world, automated translation plays a crucial role by enhancing communication, expanding companies’ outreach to diverse audiences, and swiftly and cost-effectively comprehending foreign documentation.
Sentiment Analysis
NLP in sentiment analysis, involves determining the sentiment expressed in text data. This is valuable for businesses to gauge public opinion, assess customer feedback, and make data-driven decisions based on the emotional tone of the content.
Real-time sentiment analysis allows you to keep track of social media mentions, address negative comments proactively, assess customer responses to your recent marketing initiatives or product releases, and obtain a comprehensive understanding of how customers perceive your company.
Text Summarization
NLP-driven text summarization involves condensing large volumes of text into concise summaries. This is beneficial for quickly extracting key information from articles, documents, or lengthy pieces of content, saving time and enhancing information retrieval.
Using natural language processing algorithms for data summarization comes in two methods: extraction-based summarization, where keyphrases are extracted to form a summary without additional information, and abstraction-based summarization, which generates new phrases by paraphrasing the original source. The latter approach, more prevalent and effective, is widely adopted for data summarization.
Speech Recognition
The technology of speech recognition employs NLP and AI to convert spoken language into a machine readable format like text, facilitating voice commands and transcription services.
Virtual assistants such as Siri, Alexa, and Google Assistant heavily rely on speech recognition systems. Yet, the application of speech recognition in business is expanding. Incorporating speech-to-text features into business software enables automatic transcription of calls, email generation, and even translation, showcasing diverse use cases.
Information Extraction
Natural Language processing techniques are used for extracting specific information from unstructured data sources. It helps in identifying and categorizing key elements such as names, dates, and locations from textual content, contributing to data analysis and knowledge extraction. It is also known as Named Entity Recognition (NER).
Text extraction offers the capability to identify keywords within a text, including predefined features like product serial numbers and models. Practical applications involve data entry, sorting through incoming support tickets, and recognizing particular data such as company names, order numbers, and email addresses, eliminating the need to open and read each ticket individually.
Autocorrect
Grammar-checking software and auto-correct functions heavily rely on Natural Language Processing and AI. For instance, tools like Grammarly utilize NLP techniques to enhance your writing by identifying errors in grammar, spelling, and sentence structure.
Intent Classification
Intent classification in NLP language involves determining the purpose or goal behind a user’s input. This is crucial for chatbots and virtual assistants to understand user requests accurately and respond appropriately. Apart from chatbots, intent detection can drive benefits in sales and customer support areas.
When you look at how customers talk in emails, chats, or on social media, you can find people who are likely to buy something. If you quickly figure out and organize these potential customers, you have a better chance of turning them into actual buyers.
Market Intelligence
Natural language processing algorithms are employed in market intelligence to analyze vast amounts of textual data, such as news articles and social media content. This helps businesses stay informed about market trends, competitor activities, and consumer sentiments.
Email Filtering
NLP techniques is used in a basic and early online application: email filters. It began with spam filters that detect specific words or phrases indicating a spam message. Over time, filtering improved, similar to the early versions of NLP AI.
A recent and common use of NLP AI is seen in Gmail’s sorting. The system decides if emails fit into primary, social, or promotional categories based on their content. This helps Gmail users keep their inboxes organized with important and relevant emails, making it easier to review and respond quickly.
Benefits of NLP
The adoption of natural language processing in artificial intelligence brings several benefits to both individuals and businesses:
Improved Communication
NLP enables more natural and intuitive interactions between humans and machines.
Time Efficiency
Automation of language-related tasks, such as customer support inquiries, saves time and resources.
Improved customer service
Natural language processing ai can help companies provide better customer service by automating tasks such as answering customer questions and resolving complaints.
Enhanced Decision-Making
NLP algorithms assists in analyzing vast amounts of textual data, aiding in informed decision-making.
Cross-Language Communication
Facilitates communication between speakers of different languages, promoting global collaboration.
Future of NLP
Looking ahead, the future of nlp in artificial intelligence is pretty exciting. Soon, NLP systems might get better at understanding conversations by picking up on the context, making their responses more accurate. The integration of visual and auditory cues with text, known as multimodal NLP, is on the horizon, promising a more comprehensive understanding of communication.
In the future, NLP systems might also become more personal, giving responses based on what you like and the situation you’re in. But, we need to be careful about some things. Addressing concerns related to bias, privacy, and the responsible use of natural language processing and ai will be crucial to ensuring their positive impact on society.
Conclusion
Natural Language Processing AI is now a crucial aspect of our digital experiences, changing how we interact with technology. It’s behind things like chatbots and language translation, making our lives easier. As we think about what’s coming next, the future of NLP technology looks exciting. It’s moving towards more advanced and context-aware language interactions, creating a world where machines truly get and respond to the details of how we talk.
Do you want to create AI-enabled NLP solutions for your company?
We are an award-winning technology firm with extensive expertise in creating spectacular digital success stories for a wide range of businesses.
Whether you’re a small business owner or a large enterprise, Tezeract can help you take advantage of the power of NLP in your business. From brainstorming to implementation, we’ll work with you to create custom AI solutions that save your money, time, and energy, while increasing productivity, revenue, and growth potential.
