Natural language processing (NLP) is a Machine Learning technology that allows computers to understand, interpret, and manipulate human language. Recent advancement in NLP allows businesses to leverage it for text and speech data analysis, developing virtual assistants, sentiment analysis tools, autocorrect and autocomplete platforms, multilingual media publication apps, etc.
The number of NLP use cases is growing, and so is the market. Statista reports that the Natural Language Processing market is projected to reach $36.42bn in 2024. It’s also expected to show an annual growth rate (CAGR 2024-2030) of 27.55%, resulting in a market volume of $156.80bn by 2030.
The high demand for natural language processing applications seems to be justified. Therefore, if you want to use NLP technologies to innovate your business or create a breakthrough product, this guide will help in understanding the implementation specifics and gaining valuable insights.
NLP vs LLM: Understanding the Difference
Differentiating between traditional NLP and LLMs may be challenging. In this section, we’ll sort out the details to explain what these technologies are capable and not capable of.
LLMs lie at the intersection of NLP and Gen AI. They perform NLP tasks and represent a new stage in the evolution of NLP. In the table below, you can see the comparison of traditional NLP models, LLMs, and Gen AI.
| # | Traditional NLP | LLM | Gen AI | |
|---|---|---|---|---|
| 1 | Description | Traditional NLP models often rely on specific algorithms tailored to particular tasks. They focus on predefined tasks such as part-of-speech tagging, named entity recognition, and sentiment analysis. | Type of AI model designed to process and generate text by learning language patterns from vast datasets. | AI systems designed to create new content, including text, images, code, and more, by learning from patterns in large datasets. |
| 2 | Core functionality | Analyzes and processes language data, including text comprehension, translation, and summarization. | Generates text by predicting and producing coherent language sequences. | Generates creative content in different forms based on prompts or user input. |
| 3 | Examples | Examples include bag-of-words models, TF-IDF, and more sophisticated methods like Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs). These models typically require feature engineering and are trained on smaller, task-specific datasets. They focus on predefined tasks such as part-of-speech tagging, named entity recognition, and sentiment analysis. | ChatGPT, BERT, GPT-3, PaLM (Google’s LLM). | DALL-E, ChatGPT, Midjourney, Jasper AI. |
| 4 | Data Requirement | Requires labeled data for tasks (e.g., sentiment labels, translations). | Requires large volumes of text data for unsupervised learning. | Needs large and diverse datasets (text, images, audio) for effective training. |
| 5 | Training Process | Uses traditional NLP algorithms or ML methods to learn from labeled datasets. | Trained with self-supervised learning to predict and generate text sequences. | Often trained using neural networks like GANs or transformers to create original content. |
| 6 | Technology | Can use traditional machine learning and deep learning. | Relies on transformer architecture and large-scale deep learning. | Applies neural networks, especially transformers and GANs. |
| 7 | Interactivity | Processes and analyzes existing data to draw insights or categorize it. | Generates human-like responses based on prompts. | Highly interactive, capable of creating new content based on user input and feedback. |
| 8 | Limitations | May struggle with nuanced language understanding without sufficient labeled data. Moreover, sometimes, when solving NLP tasks, we just can't rely on LLMs because of computational resources, performance, or data privacy requirements. For example, when we can't integrate with public APIs or face specific cases that can't be covered by pre-trained LLMs and don't have enough data to train or fine-tune LLM. | Can produce biased or incorrect information based on dataset biases. | Can generate inaccurate or inappropriate content without clear guidelines. |
| 9 | Use cases | Language translation, summarization, chatbot development, sentiment analysis. | Text generation, conversational agents, summarization, content generation. | Content creation (text, image, code), AI-assisted art, video generation, chatbot interactions. |
To sum it up, NLP is a broad and complex subject of AI. Traditional NLP focuses on solving language-based tasks like speech recognition, translation, and sentiment analysis. LLM, on the other hand, is a subset of the broader NLP field, and it’s applied to generate and understand text based on the training performed on large datasets. Traditional NLP relies on simple models like linear regression or decision trees, while LLMs leverage complex deep learning models. Generative AI models, in turn, can enhance NLP capabilities by offering complex prompts similar to creative human feedback. Let’s take a look at the tasks NLP can solve.
The Key 6 Natural Language Processing Tasks
NLP can solve a lot of tasks, from analyzing the emotional tone and intent of the text to finding grammatical errors. Here are some of the most common for business applications.
With the help of NLP, human speech can be converted into a form that computers are able to interpret. What makes this technology outstanding is the fascinating number of real-world applications, including spell-checkers, online search, translators, virtual assistants, and AI-powered call assessments. The MobiDev team keeps a finger on the pulse of NLP development and finds the following use cases to be the most promising:
1. Named Entity Recognition
Named entity recognition focuses on identifying entities in a sentence and classifying them into categories. Search engines can use NER to make search results more accurate. Also, news aggregators rely on the technology to categorize content, and cybersecurity companies apply it to analyze security logs and identify suspicious activity.
Example:
2. Part-of-Speech Tagging
Part-of-speech tagging implies labeling each word in a sentence as a noun, verb, adjective, adverb, or other part of speech.
Example:
3. Summarization
Summarization focuses on text shortening by identifying the important parts and creating a summary. Text summarization can be implemented in two ways:
- Extractive Summarization. Identification of the important sentences or phrases from the original text and extracting them from the text.
Example:
- Abstractive Summarization. New sentences are generated from the original text, where the generated sentences may not be present in the original text.
Example:
4. Sentiment Analysis
Sentiment analysis helps to identify the emotional intent of the text, the sentiment of a customer review, emotions behind written text or voice.
Example:
5. Text Classification
Text classification is the process of assigning predefined categories or labels to text data based on its content. Labels allow for automated text organization, filtering, and analysis.
Example:
6. Language Modeling
Language modeling is the NLP task that includes predicting the next word/character in a text/document. Language models might be used for:
- Optical Character Recognition Machine Translation
- Image Captioning
- Text Summarization
- Handwriting Recognition
- Spelling Correction
TOP 8 NLP Use Cases Across Industries
Let’s overview the most prominent applications of natural language processing technology in various business domains.
1. NLP-Powered Risk Investigation in Manufacturing
Among various AI applications in manufacturing, NLP enables efficient sorting of maintenance logs, sensor readings, and quality control reports, helping to identify patterns and potential risks in operating conditions.
How Does This Algorithm Work?
- Analyze input data to assess patterns and deviations.
- Flag potential risks and anomalies like equipment malfunctions, quality defects, or disruptions in the supply chain.
Manufacturers benefit from NLP-powered tools by taking corrective actions. As a result, they minimize downtime, reduce expenditures on repairs, preserve product quality and safety, and improve operational resilience.
2. Security Authentication With NLP
NLP technologies allow integrating advanced security techniques. By applying question generation, data scientists are able to build stronger security systems.
How Does This Algorithm Work?
- Find additional context for a user’s personal information.
- Extract relevant information (answers) using a named entity recognition model.
- Generate questions with the neural network.
- Validate a user’s answer.
At MobiDev, we built multi-modal security products using the question-generation technique. The video below shows the core ideas behind this technique:
3. NLP-Based Brand Awareness and Market Research
Developing actionable business strategies may be challenging if you don’t know how customers feel about the brand. By leveraging sentiment analysis and getting the most frequent context when your brand receives positive, negative, and neutral comments, you can improve your product and reduce weaknesses based on viable market research. Natural language processing applications analyze social media content, including customer reviews and comments, and convert them into insights.
How Does This Algorithm Work?
- Analyze an entire list of comments and classify them using a sentiment analysis model.
- Get the most frequent words and phrases from both positive and negative comments.
- Perform market research based on the data collected.
Using this algorithm makes it possible to assign a value to the output information. The value might be considered as a positive, negative, or neutral emotion. Marketing teams rely on this data when making decisions for their strategies and campaigns.
4. Chatbots for Customer Support and Engagement
NLP-powered chatbots are becoming more advanced due to their ability to perform personalized conversations and automate customer services. The basic approach involves NLP-based bots at the beginning of the interaction to take care of basic scenarios, though a human operator to handle more advanced situations.
How Does This Algorithm Work?
- Analyze customer queries to understand the intent of the customer and query context.
- Classify and route queries to initiate the appropriate support path or involve human operators.
- Generate contextual responses by recognizing keywords and phrases, aiming to address customer needs.
- Identify common issues by analyzing frequently asked questions to offer insights for potential improvements in products or services.
5. NLP-Powered Competitive Analysis
Competitor analysis and research are required steps when starting a business. This approach enables founders and investors to better understand their market, competitors, customers, and industry.
There are dozens of tools available to assist entrepreneurs in monitoring their competitors. For example, NLP-powered engines can simplify the process by gathering a list of companies and ranking them from zero to one. This rank uses a multimodal semantic field to show how closely these companies are related to each other.
6. Report Auto-Generation With the Help of NLP
AI document processing and reporting are time-consuming tasks for any business. NLP techniques convert unstructured text information into reports by applying speech-to-text dictation and formulated data entry. In this way, NLP helps design a deep learning model that identifies necessary information from unstructured text data and combines it into specific reports. This solution is sophisticated yet beneficial, especially when it comes to identifying and requesting missing data and automating the process.
How Does This Algorithm Work?
- (Preparation stage) Define a template for the report and all possible sources of information.
- Examine all data sources and find potential fillers for blank fields. This step is similar to the named entity recognition task, but it’s necessary to train the model to find its own classes.
- Deliver the report to a responsible person in a suggestion mode.
7. Real-Time Intelligence Gathering on Specific Financial Stocks
The stock market is volatile and heavily impacted by news and world events. Many companies are looking for ways to complete complex stock market analyses by accessing historical stock price data, news archives, company reports, and other relevant data.
Popular solutions like IBM’s Watson partially provide similar services. Moreover, there are other fascinating AI-based technologies already being used for stock analysis.
How Does This Algorithm Work?
- Improve understanding of a large amount of news and data found in reports similar to how sentiment analysis works.
- Label and connect news to certain companies trading the stocks.
- Sort out dependencies, such as how the stock market reacts to certain news.
- Run a continuous real-time news and reports analysis.
- Predict and notify when the stock market shifts based on recent events.
A successful solution would require a substantial amount of data science modeling using activities like NLP. And more importantly, a significant amount of computing power to calculate it all. Remember, as the business goal becomes more precise, the easier it is to solve it with high accuracy and a reasonable budget.
8. NLP-Based Recommendation Systems
NLP-based recommendation systems are revolutionizing the way users discover relevant information and products by leveraging natural language processing to interpret and respond to preferences expressed in text. Unlike traditional recommendation systems that rely on structured data and fixed filters, NLP-based recommendations can analyze user-generated content like reviews, descriptions, and search queries to provide highly personalized suggestions.
How Does This Algorithm Work?
- Analyze text-based user data, such as search queries, reviews, and preferences, using NLP techniques to capture user intent and interest.
- Extract relevant keywords, phrases, and topics from the text data. This can involve techniques like keyword extraction, topic modeling, and named entity recognition.
- Match user preferences with available content, filtering and ranking items that match the user’s interests.
- Generate personalized recommendations.
- Gather user feedback to improve accuracy and relevance by adapting to changing preferences.
NLP App Development: Challenges and Best Practices
NLP-based applications can be developed in two different ways:
- Applying ready-made LLM models
- Collecting data and training AI models from scratch for custom AI app development
The final choice depends on the project goals and requirements. Factors to be considered include the following:
- Some tasks may be very specific and cannot be solved by LLM (for such tasks, data can’t be extracted from the pre-trained models).
- Complexity of the infrastructure: LLM can be hosted on your own or use the API and pay for tokens. The first option is challenging to implement and maintain. If you use the API, there may be privacy issues and risks associated with sending sensitive data to a third-party API, especially for healthcare.
- If there are huge volumes of data that need to be driven through the model, this can be expensive with LLM.
- Developing and training models from scratch is also long and requires proven expertise. It’s important to start with AI consulting to reduce uncertainty and risks in development, finding the most effective solution in each specific case.
Build Your NLP Application With MobiDev
With over a decade in software development and years of expertise in AI, MobiDev is equipped to bring your NLP product to life. Since 2009, we’ve been a trusted vendor in the software industry, and since 2018, we’ve expanded our focus to cutting-edge AI solutions.
Our team can seamlessly integrate pre-trained models or develop custom AI solutions tailored to your unique data and business requirements. With dedicated in-house AI labs focused on R&D, our team is ready to meet any challenge and drive your product’s success. Tap into MobiDev expertise by using our consulting and engineering services!
