15+ Top Natural Language Processing Techniques To Learn in 2025

Natural language processing (NLP) is an intersection of computational linguistics, machine learning, and deep learning. Human language is often unstructured and complex, making it difficult for machines to process. NLP powers chatbots, voice assistants, and automated translations, making AI-driven interactions more human-like.

NLP techniques help bridge this gap by converting text, speech, and other forms of language into structured data that machines can analyze. This allows for applications like chatbots, language translation, sentiment analysis, and more.

15+ Essential Natural Language Processing Techniques You Should Learn in 2025

Natural language processing (NLP) plays a critical role in a wide array of industries, from customer service to social media monitoring and content analysis. The evolving nature of human language and data presents unique challenges that these techniques aim to solve, helping machines understand, interpret, and interact with text in a human-like manner. As NLP continues to grow, expect improvements in multilingual models, deeper contextual awareness, and emotion-aware AI.

The following NLP techniques form the base of many modern applications. These techniques help process text, analyze sentiment, and generate human-like responses.

1. Tokenization

Tokenization is the process of breaking down a sequence of text into smaller, manageable pieces called tokens. These tokens can represent words, subwords, or even characters, depending on the granularity required for a specific task. 

Tokenization helps in simplifying text data into units that can be processed by NLP models more effectively.

Application in Real-World and Business

In customer service chatbots, tokenization enables the system to break down user input into words or phrases, making it easier for artificial intelligence to identify intent and respond accordingly. 

It’s also widely used in text mining and sentiment analysis, where data is processed into tokens to analyze opinions, trends, and customer sentiments.

Example

In social media analytics, tokenization helps break down user comments or posts into words or phrases, allowing systems to categorize opinions, identify keywords, and understand sentiment.

Benefits and Limitations

• Benefits: Tokenization simplifies the breakdown of complex text, making it easier for systems to process. For example, in customer service chatbots, tokenization allows the system to recognize keywords like "order" or "refund," improving response accuracy.
• Limitations: In languages with complex structures, tokenization may split phrases or compound words incorrectly, leading to meaning loss. For example, "New York" could be split into two tokens, causing errors in location-based services like mapping.

Also Read: The Data Science Process: Key Steps to Build Data-Driven Solutions

2. Named Entity Recognition (NER)

NER is a technique used to identify and classify entities like names of people, organizations, locations, dates, and other specific terms in a text. This is essential for transforming unstructured text into structured data, which can be used for analysis and decision-making.

Application in Real-World and Business

NER is heavily used in financial analysis, where extracting company names, financial terms, and amounts from reports can automate data extraction. 

It’s also critical in healthcare to identify medical terms, conditions, and patient data in medical documents, ensuring faster and more accurate insights.

Example

In news aggregation, NER is used to identify important keywords such as politician names or event dates, allowing users to easily filter and read related articles.

Benefits and Limitations

• Benefits: Automates the extraction of key information such as names and dates, improving workflows in industries like legal tech. For instance, in contract analysis, NER speeds up the extraction of clauses by 40%, reducing the time spent on manual review.
• Limitations: NER can misinterpret ambiguous or uncommon terms. For instance, "Apple" could refer to the tech company or the fruit, especially in noisy data from social media, leading to misclassifications in systems like sentiment analysis.

Also Read: What is Financial Analytics & Why it is important?

3. Part-of-Speech Tagging (POS)

POS tagging involves assigning a part of speech (e.g., noun, verb, adjective) to each word in a sentence. This helps define the syntactic structure of the sentence, making it easier for machines to understand the meaning and relationships between words.

Application in Real-World and Business

POS tagging plays a vital role in machine translation and chatbot development, where understanding sentence structure helps ensure grammatically correct responses. 

It’s also useful in semantic search engines for understanding the context of a query and retrieving the most relevant results.

Example

In voice assistants like Siri or Alexa, POS tagging ensures that spoken commands are interpreted correctly. For example, distinguishing between the noun "apple" (the fruit) and the verb "apple" (to apply a coating) ensures the assistant processes commands accurately.

Benefits and Limitations

• Benefits: Helps understand sentence structure by categorizing words, such as differentiating between nouns and verbs. This is crucial for applications like email sorting, where recognizing verbs helps identify actions in customer requests.
• Limitations: In languages like German or Chinese with flexible grammar, POS tagging can misclassify words. For example, "bank" could refer to a financial institution or a riverbank, leading to confusion in text processing.

4. Sentiment Analysis

Sentiment analysis is the process of analyzing text to determine the sentiment or emotional tone expressed, typically categorizing it as positive, negative, or neutral. This analysis helps in understanding public opinions, customer feedback, and market trends.

Application in Real-World and Business

Sentiment analysis is crucial in social media monitoring and brand reputation management, allowing businesses to track public sentiment in real time. It’s also used in product feedback analysis, where sentiment extraction from reviews can drive product improvements or marketing strategies.

Example

In retail, sentiment analysis can be applied to customer reviews to assess the overall satisfaction with a product. Brands can instantly detect if customers are generally satisfied or facing recurring issues.

Benefits and Limitations

• Benefits: Provides businesses with real-time insights into customer sentiment, enabling swift actions. For example, in social media monitoring, sentiment analysis can help brands quickly identify negative feedback and address customer complaints.
• Limitations: Sarcasm or irony can confuse sentiment analysis tools. A comment like "Great job on the project!" can be misclassified as positive, even when it's sarcastic, leading to incorrect sentiment assessments in social media analytics.

Also Read: Exploring the 6 Different Types of Sentiment Analysis and Their Applications

5. Dependency Parsing

Dependency parsing analyzes the grammatical structure of a sentence, identifying the dependencies between words, which defines the syntactic structure of the sentence. This method helps in understanding the relationship between words and extracting the meaning from complex sentences.

Application in Real-World and Business

In search engines, dependency parsing enables a more accurate understanding of user queries by recognizing how different words relate to each other. It also plays a key role in machine translation, ensuring that the translation respects sentence structure and context.

Example

In customer service, dependency parsing can help chatbots better understand complex customer queries. For example, the sentence "I want to know the status of my order" would be parsed to understand that "order" is the object of interest.

Benefits and Limitations

• Benefits: Helps understand the deeper structure of sentences, allowing for accurate interpretation of relationships. For example, in legal documents, dependency parsing can identify how clauses relate to each other, improving contract review processes.
• Limitations: Parsing long, complex sentences can be computationally expensive, especially in languages like Japanese, where flexible word order can cause errors, impacting text-to-speech or translation applications.

6. Text Classification

Text classification is the process of categorizing text into predefined groups based on its content. It is widely used in applications like sorting emails into spam vs. non-spam., content categorization, and topic identification.

Application in Real-World and Business

In email marketing, text classification helps automatically sort and categorize customer queries or feedback, routing them to the correct department. It is also used in news aggregation, where articles are classified based on topics such as politics, sports, or technology.

Example

In content recommendation systems, text classification enables personalized suggestions by categorizing articles, blogs, or videos based on user preferences and behavior.

Benefits and Limitations

• Benefits: Automates categorization of large volumes of text, saving time in industries like publishing. For instance, it can categorize thousands of articles daily into topics like politics, sports, or health, improving content management.
• Limitations: When text overlaps categories, such as an article about both "sports" and "health," classifiers may struggle to categorize it correctly, leading to misclassifications in news aggregators or content recommendation systems.

Also Read: What is Data Mining? Techniques, Examples, and Future Trends in 2025

7. Machine Translation

Machine translation automatically converts text or speech from one language to another using algorithms. Unlike traditional translation methods, it uses computational models to improve translation accuracy. While machine translation automates language conversion, improving efficiency, it still faces challenges in contextual accuracy.

Application in Real-World and Business

Machine translation is heavily used in global communication, enabling businesses to localize content, expand into new markets, and facilitate cross-language communication. It’s also used in customer support, where real-time translation allows businesses to assist customers in different languages.

Example

In e-commerce, machine translation helps convert product descriptions, reviews, and support materials into various languages, making it easier to reach a global customer base.

Benefits and Limitations

• Benefits: Breaks down language barriers in global communication. For instance, translation apps enable tourists to navigate foreign countries without language difficulties, improving their experience in real-time.
• Limitations: Machine translation may struggle with idiomatic expressions, such as "kick the bucket," which translates literally rather than capturing the meaning, leading to errors in user interfaces for global applications.

8. Topic Modeling

Topic modeling identifies the underlying themes or topics in a collection of text documents, grouping similar items for better analysis and understanding. It helps in discovering hidden structures within large datasets.

Application in Real-World and Business

In research and publishing, topic modeling is used to categorize large sets of documents, making it easier for researchers to find relevant papers or articles. It’s also helpful for businesses to understand the key themes emerging from customer reviews or social media discussions.

Example

In academic research, topic modeling helps in grouping related studies or articles by topic, making it easier to find relevant research based on a specific theme.

Benefits and Limitations

• Benefits: Uncovers hidden patterns and insights within large datasets, improving content management systems. For example, it can cluster articles on similar topics, enhancing content discovery in digital libraries.
• Limitations: Topic modeling can miss niche topics not well-represented by general terms. For example, it may fail to detect emerging biotechnology trends from broader healthcare-related data, reducing accuracy in specialized research databases.

Also Read: Beginners Guide to Topic Modelling in Python

9. Word Embeddings

Word embeddings represent words as vectors in a continuous vector space where semantically similar words are closer together. This allows NLP models to understand the relationships between words, enabling more accurate text analysis.

Application in Real-World and Business

Word embeddings are used in recommendation systems, where they help understand relationships between words and products, allowing businesses to suggest items that match a user’s preferences. 

They are also fundamental in search engines, improving result relevance based on user queries.

Example

In online retail, word embeddings can help recommend products by identifying similarities between item descriptions, enhancing the user experience.

Benefits and Limitations

• Benefits: Improves understanding of word relationships, enabling more accurate predictions. For instance, in e-commerce, word embeddings help recommend products based on semantic similarities between product names and user preferences.
• Limitations: Word embeddings require large datasets to be effective. On smaller datasets, the relationships between words may not be accurately captured, leading to imprecise results in applications like search engines or recommendation systems.

Also Read: Evolution of Language Modelling in Modern Life

10. Language Generation

Language generation involves creating human-like text from a given input, enabling applications to automatically produce content such as articles, emails, or chatbot responses.

Application in Real-World and Business

Language generation is widely used in content creation, where it can automatically generate reports, blog posts, and even product descriptions. It's also used in customer support chatbots to provide natural, conversational responses.

Example

In e-commerce, language generation models like GPT are used to generate personalized email marketing content, helping businesses connect with customers on a more personal level.

Benefits and Limitations

• Benefits: Speeds up content creation and personalization at scale. For instance, in digital marketing, AI can generate personalized emails for thousands of customers, increasing engagement without manual effort.
• Limitations: Language generation can sometimes lack depth or creativity, resulting in repetitive or generic content, such as automated product descriptions that feel robotic or fail to engage readers effectively.

Also Read: GPT-4 vs ChatGPT: What’s the Difference?

11. Text Summarization

Text summarization involves condensing large blocks of text into concise summaries while retaining the core meaning and main points. There are two main types: extractive summarization and abstractive summarization. 

Extractive summarization selects key sentences directly from the original text. Abstractive summarization generates a new summary using a language model that mimics human summarization.

Application in Real-World and Business

In news aggregation, summarization helps users quickly get the essential information from long articles or reports. It’s also used in research to condense academic papers, enabling readers to digest complex topics without reading the entire document. 

In content curation, summarization makes it easier to manage and deliver large volumes of content efficiently.

Example

In legal technology, text summarization helps lawyers and legal professionals by summarizing long legal documents, allowing them to quickly understand the case details without reading through pages of legal text.

Benefits and Limitations

• Benefits: Saves time and effort by condensing lengthy content into key points, making it easier for users to quickly access important information. For instance, in legal tech, summarizing long contracts helps lawyers quickly identify key clauses.
• Limitations: Abstractive models may overlook subtle nuances or domain-specific details, leading to incomplete or misleading summaries, such as missing critical technical terms in research papers.

12. Question Answering (QA)

Question answering (QA) involves extracting an answer from a given context (often in the form of a document or a set of documents) in response to a user’s query. 

It can be divided into extractive (pulling directly from the text) and abstractive (generating new answers) types of models.

Application in Real-World and Business

QA systems are frequently used in customer support, where automated systems respond to customer inquiries based on a knowledge base. They are also applied in enterprise search engines, enabling users to search for specific answers within large volumes of data or documentation.

Example

In e-commerce, a QA system can answer questions such as “What is the return policy for shoes?” by scanning a store's FAQ page and providing the most relevant response instantly.

Benefits and Limitations

• Benefits: Provides quick, relevant answers by extracting specific information from documents, improving customer service efficiency in industries like healthcare where patients need fast access to treatment guidelines.
• Limitations: QA systems can struggle with ambiguous or complex queries, such as "What happens when X and Y meet?" if the context is unclear, leading to inaccurate or incomplete responses in customer support scenarios.

Also Read: 30 Natural Language Processing Projects in 2025 [With Source Code]

13. Coreference Resolution

Coreference resolution involves identifying expressions that refer to the same entity in a text. 

For example, in the sentence, Apple announced its latest iPhone. The company claims it’s the best smartphone yet." (Here, "Apple" and "the company" need to be correctly linked.)

Application in Real-World and Business

In document summarization, coreference resolution ensures that all references to the same entity are grouped together, creating a clearer summary. 

It’s also used in machine translation to preserve context when translating between languages, ensuring that pronouns and proper nouns are correctly referenced.

Example

In automated legal document analysis, coreference resolution helps legal professionals track references to parties or clauses across long documents, improving the accuracy of contract reviews.

Benefits and Limitations

• Benefits: Ensures reference consistency across a document, improving the coherence of the output. For example, in legal documents, it helps to maintain consistency when referring to specific parties or entities.
• Limitations: In unclear or ambiguous texts, coreference resolution may incorrectly link terms, such as confusing "he" for the wrong subject, leading to confusion in the interpretation of complex legal or academic texts.

14. Speech Recognition

Speech recognition converts spoken language into text by analyzing sound patterns and matching them to a predefined language model. This technology enables systems to understand and process human speech, allowing for voice commands, transcription, and speech-to-text applications.

Application in Real-World and Business

In virtual assistants (like Siri or Alexa), speech recognition powers voice commands, enabling hands-free interactions. It is also used in medical transcription, where doctors can dictate notes instead of typing them, significantly speeding up documentation processes.

Example

In call centers, speech recognition helps automate customer support interactions by converting spoken requests into text, allowing AI systems to analyze customer inquiries and respond accurately.

Benefits and Limitations

• Benefits: Provides convenience, particularly for users with disabilities, enabling hands-free interactions like dictating notes or controlling devices through voice. This is helpful in environments like healthcare, where hands-free data entry improves efficiency.
• Limitations: Speech recognition struggles in noisy environments or with strong accents, misinterpreting words like "car" as "card," causing errors in transcription, which affects applications like voice assistants or transcription services.

Also Read: What is Speech Recognition in AI: Exploring Core Functions, Applications, and its Future Impact

15. Text-to-speech (TTS)

Text-to-speech (TTS) technology converts written text into spoken language, creating a synthetic voice that reads the text aloud. 

Unlike simple speech recognition, TTS focuses on producing natural-sounding speech from text input, making it essential for creating conversational AI systems.

Application in Real-World and Business

TTS is used in navigation systems to provide verbal instructions to drivers, in customer service for IVR (Interactive Voice Response) systems, and in assistive technologies for visually impaired users to read out written content.

Example

In online education, TTS allows textbooks, articles, and assignments to be read aloud, making learning more accessible for students with visual impairments or learning disabilities.

Benefits and Limitations

• Benefits: Enhances accessibility and user engagement by converting text into audio, allowing users to multitask. For example, TTS in e-books helps users listen to content while driving or exercising.
• Limitations: TTS can sound mechanical and robotic, especially in earlier versions, failing to reflect human-like intonation or emotion. This limitation is evident in earlier navigation systems, where directions sounded unnatural.

Also Read: Top 10 Speech Recognition Software You Should Know About

16. Speech Synthesis

Speech synthesis refers to the generation of artificial human-like speech from text, focusing on creating natural-sounding, human-like speech from machine-generated content. It's essential for interactive systems that need to communicate with users.

Application in Real-World and Business

Used in IVR systems, video games, and virtual assistants, speech synthesis allows for seamless verbal communication. It also helps people with disabilities by providing voice output for visually impaired individuals or those with limited physical mobility.

Example

In virtual games, speech synthesis helps characters interact with players using dynamic, conversational speech, adding realism and improving the user experience.

Benefits and Limitations

• Benefits: Provides natural interaction in voice-based systems, enhancing user experience in devices like smart assistants. It allows for more fluid, human-like conversations with devices like Alexa or Google Assistant.
• Limitations: The quality of synthetic speech can be artificial and lacking in natural intonation, making dialogue sound mechanical, especially in emotionally complex scenarios such as therapy apps or storytelling.

Also Read: Top 6 Speech Processing Projects & Topics For Beginners & Experienced

17. Language Modeling

Language modeling involves predicting the probability of a word sequence in a given text, enabling machines to generate coherent, contextually appropriate sentences. It’s a core technique for text generation and machine translation.

Application in Real-World and Business

Language models are at the core of text generation, auto-completion, and chatbots. They are also used in automatic translation and speech recognition, where understanding the sequence of words is key to accurate results.

Example

In content creation, language models help automate the generation of product descriptions or articles, assisting marketers in scaling content production without sacrificing quality.

Benefits and Limitations

• Benefits: Provides context-aware generation, improving the fluency and coherence of machine-generated content. For example, in marketing, NLP models can generate more relevant ad copy based on customer data.
• Limitations: Language models often require extensive datasets and significant computational power. They can be less effective in specialized areas like legal or medical domains, where they may produce inaccurate or general predictions.

Also Read: Artificial Intelligence Applications: 13 Exciting Real-World AI Applications

With NLP-driven AI shaping industries from finance to healthcare, understanding these techniques gives you a competitive edge. Once you understand these foundational methods, you’ll be equipped to apply them strategically to your projects. 

Next, let's explore how to select the right NLP techniques that best align with your goals and data needs to maximize your success.

How to Select the Right NLP Techniques for Your Success?

Selecting the right NLP techniques is essential for ensuring that your project is both efficient and successful. The process requires understanding your project goals, the type of data you are working with, and the specific requirements of your application. 

Choosing the wrong technique can lead to poor model performance, wasted resources, or inaccurate results. 

Here are some factors to keep in mind when choosing NLP techniques: 

Factors to Consider When Choosing Techniques

When selecting NLP techniques, understanding your project’s goals, data type, and available computational resources is crucial. Here are some tips that can help you make this choice. 

1. Project Goals

Understand the objective of your project. Are you analyzing sentiment, generating text, translating languages, or extracting specific information? 

Each goal will require different techniques. For example, sentiment analysis typically uses text classification techniques, while language generation would need language modeling.

2. Data Type and Quality

The type of data you are working with (e.g., structured vs. unstructured, clean vs. noisy) significantly impacts the techniques you should use. 

Text summarization might be used when working with large bodies of text, while tokenization is a necessary step in nearly every NLP task, especially when data is unstructured.

Also Read: Structured Vs. Unstructured Data in Machine Learning

3. Computational Resources

Some NLP techniques are computationally expensive. For instance, deep learning-based models like transformers for language generation can require significant resources. 

Similarly, simpler methods like POS tagging or NER might be more efficient for smaller datasets or less complex tasks.

Also Read: Top 16 Deep Learning Techniques to Know About in 2025

4. Accuracy vs. Speed

Depending on your needs, you may need to prioritize either speed or accuracy. Simple algorithms like rule-based methods might offer faster results. 

On the other hand, deep learning-based approaches like BERT or GPT models may provide better accuracy at the cost of longer processing times.

Different NLP techniques work differently for different purposes. In the next section, let us have a look at some common techniques and their utility. 

Popular NLP Techniques Based on Use Cases

Different NLP techniques serve distinct purposes across industries. By understanding these use cases, you can choose the best technique for your project, ensuring the most efficient and effective results. 

Each technique has its strengths depending on the problem you're tackling. For instance: 

• Text Classification:

Useful for categorizing documents or identifying intent in user queries. Common in email spam filtering, social media monitoring, and customer feedback analysis.

• Named Entity Recognition (NER):

Helps identify and classify key entities like names, organizations, and dates. Essential in applications like business intelligence, legal document analysis, and medical record parsing.

• Sentiment Analysis:

Often used in market research and customer service, it helps businesses gauge the mood and opinions expressed in text, such as product reviews, social media posts, or customer feedback.

• Text Summarization:

Suitable for summarizing long articles or documents, ideal in news aggregation, research paper summarization, and content curation.

• Machine Translation:

Translates text from one language to another. Widely applied in global business expansion, content localization, and customer service for cross-language support.

• Question Answering:

Used to answer specific questions based on available text. Found in customer service chatbots, help desks, and automated FAQs.

Understanding the popular NLP techniques for various use cases is key to applying the right methods in your projects. However, it’s equally important to recognize potential future trends and technologies.

Let’s now see the latest technical advances in NLP and they will revolutionize the future.

Emerging NLP Trends to Watch in 2025

Emerging NLP trends are shaping the future of language processing across industries. By staying informed about these innovations, you can integrate the latest advancements into your projects for enhanced accuracy and efficiency. Each trend brings unique advantages, addressing different challenges in NLP.

Here are some key future trends in the NLP field: 

• Zero-shot and Few-shot Learning
  • Enables models to perform tasks with minimal or no task-specific training data.
  • Example: GPT-4 can execute complex tasks like question answering or text summarization with little input, making it highly adaptable and efficient.
• Retrieval-Augmented Generation (RAG)
  • Combines search techniques with NLP models to enhance the accuracy and relevance of responses.
  • Provides more contextually-aware answers by pulling in external information, making it ideal for real-time question-answering systems.
• Neurosymbolic AI
  • Integrates deep learning with rule-based methods for better reasoning and interpretability.
  • Helps NLP models understand logic and make decisions based on both data-driven insights and pre-programmed rules, improving model transparency.
• Self-Supervised Learning in NLP
  • Allows models to learn from raw, unlabeled data, significantly reducing the need for labeled datasets.
  • Makes NLP systems more scalable and effective in handling diverse and large datasets, driving advancements in fields like text mining and language understanding.

These emerging trends are redefining the landscape of NLP, providing powerful techniques that promise to improve accuracy, adaptability, and interpretability in language-based AI applications.

While emerging NLP trends offer exciting possibilities for the future, it's equally crucial to be aware of the common challenges you may face when applying these techniques. 

Let’s now explore some of the typical pitfalls in NLP projects and how to avoid them to ensure successful implementation and optimal performance.

Common Pitfalls and How to Avoid Them

In NLP projects, avoiding common pitfalls can make or break your model’s performance. Issues like poor data preprocessing, overfitting, and neglecting the right evaluation metrics can lead to inefficiencies or inaccurate results. 

By addressing these challenges early on, such as ensuring proper data cleaning or using cross-validation, the accuracy and robustness of the NLP model can be enhanced.

Common challenges include: 

Also Read: Machine Learning vs NLP: Difference Between Machine Learning and NLP

Exploring the top NLP techniques you should master in 2025 sets the foundation for your journey in the field. As you deepen your understanding, having the right support and resources becomes crucial. 

Let’s now look at how upGrad can enhance your NLP expertise and guide you toward success in this evolving field.

How upGrad Supports Your Natural Language Processing Journey?

upGrad’s Natural Language Processing (NLP) programs provide hands-on training, from mastering foundational techniques to advanced methods for developing and optimizing NLP models.

Here are some of the top upGrad courses to enhance your NLP expertise:

• Fundamentals of Cloud Computing
• Introduction to Natural Language Processing
• Artificial Intelligence in the Real World
• Fundamentals of Deep Learning and Neural Networks
• Post Graduate Certificate in Machine Learning & NLP (Executive)
• Post Graduate Certificate in Machine Learning and Deep Learning (Executive)
• Professional Certificate Program in AI and Data Science

For personalized guidance, connect with upGrad’s counselors or visit a nearby upGrad career center. With expert mentorship and an industry-aligned curriculum, you’ll acquire the skills to tackle complex NLP challenges and advance your career in AI and data science!

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