What do You Need to Know About Natural Language Processing?

In a world where data is real money, businesses can sideline 80% of unstructured data in text, images, audio, and videos. As this data can’t be presented in a tabular form, extracting key information from such data remains a key challenge for businesses. However, the invention of NLP, or Natural Language Processing changed the dynamic. 

It works as a viable means to analyze unstructured data with the help of techniques such as entity recognition and sentiment analysis so that businesses can unlock the hidden value within this vast data. 

Natural Language Processing Meaning 

Natural language processing is a sub-field of artificial intelligence and refers to enabling machines to generate text and speech content through the use of rule-based human language modeling, statistical modeling, deep learning, and machine learning. By combining LLMs or Large Language Models with image generation models, NLP laid the foundation of generative AI, a sub-field of AI valued at $45 billion. 

The natural language processing models are the backbone of NLP we see around us. These models are used for training the machines so that they can process, interpret, and generate human-like content.  At the beginning of the NLP era, we had traditional natural language processing models such as N-grams, Hidden Markov Models, and Conditional Random Fields that were mostly statistical and rule-based. They failed to understand complex data. 

Modern-day natural language processing models are deep-learning based and can learn complex patterns from large amounts of data. Natural language processing with transformers is capable to process data in parallel and capture long-range dependencies effectively. NLP enables machines to detect dialects, slang, and context irregularities that humans make while conversing through text and speech. This leads to better human language processing. 

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How Natural Language Processing Works? 

At the core functional level, NLP combines computational linguistics with machine learning algorithms and deep learning. Each of these components has a different role to play. Let's break down each of them for you. 

Computational Linguistics

It involves using data science to analyze speech and language. Let’s assume you tell a computer the sentence "The cat sat on the mat." With the use of computational linguistics, the computer will break down the sentence into different parts to process it accurately. 

Now, computational linguistics will process this information in two ways. 

1. Syntanical Analysis- In this technique, word order and sentence structure are considered. Is the sentence grammatically correct?  What is the subject of the sentence? What is the noun? 
2. Semantic Analysis-  It focuses on the meaning of each word and the whole sentence. For instance, it will try to figure out does the "sat on" means the cat is resting or crushing the mat.

Machine Learning

Machine learning technology enables machines/computers to establish a solid understanding using the sample data. This lets machines understand the various human language features such as metaphors, sarcasm, variations, emotions, etc. It leads to identifying the patterns within a human conversation and establishing a relationship between different words and their meaning. 

In the above example, machine learning will break down the complete sentence, The cat sat on the mat, into small units and will convert them into vectors. Going forward, machine learning algorithms such as Support Vector Machines or Naive Bayes use these vectors to classify or analyze. 

Deep Learning

Deep learning is a type of machine learning that is used for complex sentences. It features neural networks, a structure inspired by neurons present in human brains, to develop a deeper understanding by considering the context. 

Deep learning models often use word embeddings that enable vectors to semantic and syntactic relationships between words. For example, the word embeddings for "cat" and "dog" might be similar because they both represent animals.

RNNs are traditional deep learning models, ideal for text-like processing of sequential data. However, we now have advanced transformer models for NLP tasks. They process the entire sentence at once, and capture relationships between all words. This allows them to understand more complex language structures.

Must Read: Deep Learning Detailed Guide 

NLP Implementation 

Implementation of NLP is a systematic approach and begins with gathering and preparing unstructured data from sources such as email, data warehouses, surveys, and other business resources. Here are the steps to follow for implementing NLP. 

Data Processing 

This involves using different pre-processing techniques so that clean and useful data is used for training. Tokenization is most commonly used for pre-processing of data. It involves breaking long text into smaller units, tokens, such as words or subwords, and structuring the entire database. This is the primary step of NLP implementation. 

Other than this, many other techniques like stemming & lemmatization, stop word removal, and part-of-speech tagging are also used in data pre-processing. 

Development

Next comes NLP system development that are mainly two types of algorithms; rule-based and machine-learning-based. 

Rule-based System- This type of system requires defining rules to perform different tasks. Domain experts define these rules and embed them into the NLP applications. These algorithms are suitable to solve problems with well-defined domains and can be articulated by rules. 

Such types of algorithms are developed by following the below-mentioned steps. 

1. The process starts with understanding the problem and its rules to gather relevant understanding. 
2. Based on the gained understanding, certain rules are refined to encapsulate the decision-making.  
3. Rule-based systems are tested with sample data to check whether they are behaving appropriately in different scenarios. 
4. Review the performance of the system, gather the performance feedback, and refine the system accordingly. 
5. Deploy them in real-world natural language processing applications post reviewing their performance. 

Machine Learning-based System- This type of system is powered by machine-learning technology to uncover hidden patterns and establish a relationship between different data. Machine learning systems follow programmed rules to learn from their recent interactions and modify their behavior accordingly, making them suitable for solving complex problems. 

Machine learning systems are developed in the below-mentioned steps. 

1. Based on the nature of the task and data structure, developers select the fitting machine learning algorithm. 
2. The selected model is then trained using the pre-processed data. At first, data is fed into the model in input-output pair and then the data parameters are adjusted to reduce the loss function. 
3. Trained models are tested for effectiveness using suitable metrics and validation methods. 
4. Techniques like random search, grid search, or Bayesian optimization are used to fine-tune the hyperparameters of the models. 
5. When the model’s performance is satisfactory,  it goes into the development of NLP-based applications that can provide responses using new or unseen data in human language.  

Must Read: Role of AI Agents in Business Customer Service

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Natural Language Processing Approaches 

Natural Language Processing has evolved significantly over the years, with various approaches emerging to tackle the complexities of human language. Below, you will find some of the most prime natural language processing approaches. 

Rule-Based Approach

This NLP approach relies on hand-crafted rules and linguistic knowledge about a given data. It grants great control over language processing and works best in well-defined domains. However, it is inflexible to language variants and demands exhaustive linguistic expertise, which is not a feasible option for one. 

Statistical Approach

Statistical natural language processing involves using statistical models and medical learning to train the NLP models.  While this approach enables natural language processing applications to handle large datasets and adapt to various language variations, it works best with labeled data. 

Deep Learning Approach

Natural language processing with deep learning uses neural networks to learn complex patterns, hidden in a given database. It can capture semantic and syntactic information effectively. However, it demands high computational resources, along with large datasets. 

Supervised NLP

Supervised NLP involves training the natural language processing models with labeled or known input and then producing outputs using the gained intelligence.  

Unsupervised NLP

Unsupervised NLP is mainly based on a statistical language model that enables NLP to predict the pattern of non-labeled input data. 

Natural Language Understanding

Natural language understanding or NLP is a sub-field of NLP and focuses on finding meanings behind sentences. Machines must find different meanings of the same words, used in different contexts. 

Natural Language Generation

Natural language generation or NLG aims to capable machines to generate conversations like humans and perform tasks such as text summarization, machine translation, and dialogue generation. 
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Natural Language Processing Applications for Businesses 

NLP enables businesses to use computers or machines to assess human-language data for a wide range of applications. 

Sentiment Analysis

Industries such as retail, e-commerce, and consulting can use NLP applications to analyze text data, present in human language, to determine the hidden customer sentiments and customer opinions. They can read reviews, analyze survey responses, and collect feedback from social media posts, using NLP, to understand customer opinions about a specific product or overall brand. 

The finance & banking industry can also use NLP to determine borrowers’ intent and sentiments, which is crucial to reducing bad debts.  AI-enabled NLP has already helped this industry to up the debt collection rate by 15-20%. 

Text Classification

Text classification is crucial to label or classify different texts for easy handling. NLP models can help businesses classify email, customer queries, support tickets, and other forms of text to categorize them into different classes or categories. 

Named Entity Recognition (NER)

NLP has Named Entity Recognition or NER capabilities that enable to NLP identify and classify named entities in text, such as people, organizations, locations, dates, and quantities. With this, businesses can extract key data from reports, social media posts, news articles, and other types of data resources. 

This is also useful for the customer support team as they can use NLP applications to identify relevant entities to answer questions. Money lending businesses can also use NLP-powered NER to identify potential risks or threats mentioned in application forms. 

Machine Translation

NLP helps in machine translation, the processing of automatically translating texts from one language to another. This is crucial for global businesses offering multilingual customer support, document translation, and market research. 

The education industry can use machine translation for language learning whereas the e-commerce industry requires machine translation to optimize their website for different types of audiences. 

Text Summarization

NLP applications are useful for summarizing long-form content such as reports, research papers, and many more into shorter summaries while preserving the key information. 

The media industry uses NLP for text summarization to generate concise summaries of lengthy articles for busy readers whereas the healthcare industry uses it to create summaries of health reports, 

Chatbots and Virtual Assistants

Almost every industry, including healthcare, e-commerce, and retail, uses NLP for the development of chatbots and virtual assistants to understand and respond to user queries in natural human language. 

Topic Modeling

NLP enables machines or computers to abstract different types of topics occurring in a collection of documents. This process is known as topic modeling and is useful for discovering hidden semantic structures within a text corpus. 

Information Extraction

NLP can assist businesses in extracting a particular set of information for a given unstructured or semi-structured data automatically. Topic models are used by businesses to extract key information from:

1. Customer queries accurately and route them efficiently
2. Legal documents for compliance 
3. Financial reports for detailed analysis 
4. Competitor information, product profiles, and customer sentiments for market research 

Document Classification

NLP enables effective and fully automatic document classification into predefined classes. Businesses can train NLP models on a label dataset and use it to classify new documents into predefined categories. 

This is useful for every day business-related tasks such as customer support, email filtering, knowledge management, market research, and risk management. 

NLP Use Cases by Industry

As NLP enables machines to process human language with utmost precision and swiftness, it has diverse use cases across industries. 

Finance

The finance industry can use NLP applications to perform sentiment analysis, fraud detection, and risk assessments. This industry can empower AI agents with NLP capabilities to identify hidden patterns through text analysis, evaluate legal documents for potential risk, and assist customers with financial transactions. 

‍Must Read: What are AI agents? Types & Benefits 

Healthcare

Integration of NLP in AI agents for the healthcare industry leads to automatic data extraction from patient records, test reports, research papers, and even clinical trials. 

Natural language processing for healthcare further assists in drug discovery by analyzing and summarizing scientific literature. 

Chatbots for the healthcare industry such as Florance and Ada Health are already powered by NLP and are helping patients to get accurate and immediate information. 

Marketing and Sales

Marketing and Sales verticals of a business use NLP for summarizing customer reviews and surveys to understand product perception and fine-tune accordingly. NLP is crucial for monitoring brand reputation and understanding customer perception through customer feedback on social media and via emails. 

NLP can also help these departments identify customer groups based on language patterns and preferences and segment them accordingly. 

‍Must Read: How AI Agents Can Redefine Marketing & Sales 

Education

The education industry can use  NLP to design intelligent tutoring systems that can personalized learning experiences for each user after reviewing past learning processes. Schools and other educational bodies can use the NLP to assess student assignments and provide feedback. 

Machine translation capabilities of NLP enable the education industry to create the same learning material in different languages automatically whereas text summarization abilities are ideal for students to understand the crux of the matter while dealing with long-form content. 

Retail 

The retail industry can use NLP for sentiment analysis and market research. It can use NLP to enhance and business-customer interaction by empowering AI chatbots with NLP. Online retailers can use NLP to enhance the functionality, accuracy, and speed of voice-based search, which accounts for 1 billion searches per month. 

NLP for AI development in retail can also identify buying preferences and patterns and help retailers to make relevant product recommendations. 

Manufacturing

Natural language processing with deep learning enables the manufacturing industry to do predictive maintenance by analyzing maintenance logs data, sensor data, and technician notes.  NLP applications can review product performance reports, reviews, and maintenance reports to assess the real-world the performance of a product. 

It can even extract information from customer feedback and automate routine tasks such as order processing and inventory management. 
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In Conclusion 

Natural language learning applications are promising and are about to hit new horizons by leveraging transformer-based models such as BERT, GPT-3, and LLaMA. NLP is witnessing unprecedented advancements in contextual understanding and response generation. 

OpenAI, the inventor of ChatGPT, has now OpenAI embedding models to offer that will use big data and advanced algorithms to have a nuanced understanding of the data. It will process words along with the context, resulting in better vector representation. 

Do you struggle to capitalize on the NLP capabilities for your businesses and use them to revolutionize operations, break language barriers, and unlock untapped insights? Let Ampcome be your partner in revolutionizing your business.

With decades of experience in AI development, including NLP,  we can help you design natural language processing applications that seamlessly bridge the gap between humans and machines while allowing you to drive operational efficiency and uncover valuable insights. 

Book a free consultation to learn how to use NLP to transform your business. 

FQAs 

What are the Applications of Natural Language Processing?

NLP has a wide range of applications across various industries. For instance, healthcare, retail, education, and the FinTech industry can design chatbots and virtual assistance to handle customer queries. These and many more industries can use NLP-powered AI agents to perform tasks like sentiment analysis, speech recognition, information extraction, text summarization, spam filtering, and information extraction. 

Is Natural Language Processing Machine Learning?

Yes, natural language processing is related to machine learning. It is a sub-field of machine learning and uses machine learning algorithms to enable computers from a given data and understand human language.  

What is Natural Language Processing in AI? 

Natural Language Processing refers to the branch of artificial intelligence that aims to understand human language the better way. It involves using different types of datasets to teach computers to understand, interpret, and generate language like humans. 

What are Natural Language Processing Techniques?

There are multiple natural language processing techniques such as tokenization, sentiment analysis, dependency parsing, part-of-speed tagging, stemming, and named entity recognition.

Is Natural Language Processing Deep Learning?

Natural language processing in deep learning uses neural networks and nd models like RNNs and Transformers to understand the context of a given data. It has significantly advanced NLP capabilities in machine translation and text generation tasks.  It uses It used neural networks and models like RNNs and Transformers to understand the context of a given sentence. 

Is Natural Language Processing Generative AI?

Generative AI is an application of NLP. Generative AI tools such as ChatGPT and Gemini use NLP to understand the voice and written commands of humans and generate human-understandable text. 

What does Natural Language Processing Do?

NLP can translate texts in different languages, summarize reports and research papers, understand the context of communication, and perform sentiment analysis. 

Why is Natural Language Processing Important for Businesses?

NLP is crucial for businesses as it enables them to process unstructured data and improve many standard workflows. For instance, natural language processing chatbots and virtual assistants can understand the context of customer inquiries efficiently. NLP can help the education industry to create personalized content where as the retail industry uses it to polish voice search. It can even automate document summarization and information extraction, resulting in saving time and resources.