Natural Language Processing Explained: How NLP is Transforming Technology in 2025

“Ever wondered how your smartphone understands slang, accents, and even emotions?”

Natural language processing (NLP) doesn’t just recognize words - it comprehends intent, context, and sentiment to make digital conversations more fluid and intuitive. Think Siri or Alexa for instance. By understanding voice commands and tone, they respond with relevant information or actions. That’s the magic of NLP, the brains behind smart translators and savvy chatbots that feel almost… human. 

In 2025, we are witnessing, in real-time, the evolution of this technology, where it can now catch hints of sarcasm, decode regional dialects, and spot frustrations in your messages. And that means even more for businesses riding the wave of seamless customer service. Let’s dive into what NLP is and how it’s set to transform our tech landscape this year and beyond!

What Is Natural Language Processing

A subfield of Artificial Intelligence, NLP enables computers to understand, interpret, and respond to human language to drive meaningful conversations. But you ever wonder how man and machine can communicate seamlessly and what powers the conversation? The answer lies in the following three elements that process text and speech data for various applications today:

• Computational linguistics - This provides the foundational rules for language, such as grammar, syntax, and semantics, so computers can understand language structures and meaning. It also creates frameworks and models language rules for computers to easily interpret conversational language. 
• Machine learning - This technology trains ML algorithms on large datasets of languages so computers can recognize nuances and context in speech or text. The goal behind NLP in machine learning is to improve accuracy and understand complexities in speech like sarcasm, tone variations, and metaphors. 
• Deep learning - An advancement to machine learning, deep learning mimics the human brain’s structure using neural networks to identify subtle patterns or changes in language. For example, deep learning models like GPT-4 enable sophisticated language comprehension for advanced NLP tasks like speech recognition, language translation, and sentiment analysis. 

Combining the trio of technologies above, the industrial potential for natural language processing is massive, with the overall market expected to grow at an annual rate of 24.76%, driven by increased adoption even in household appliances. (Source) Whether you input commands to a robotic assembler or simply visit a website with an AI chatbot that pops up, this tech is evolving into an inherent part of our tech use today. Let’s dive into some examples:

• Language translators - Use NLP to analyze context in multiple languages for accurate translation. 
• Virtual assistants - Use intent classification and speech-to-text conversion to understand user commands and perform tasks efficiently.
• Customer service chatbots - By interpreting textual commands from users, they handle inquiries and provide support whenever needed. 

Natural Language Processing Evolution

NLP capabilities have evolved beyond comprehension to a point where the boundaries between human-machine interaction are becoming thin over time. And there have been plenty of impactful breakthroughs that transformed the way computers understand and generate human language. 

For example, Pathways Language Model (PaLM), a state-of-the-art LLM by Google that consists of 540 billion parameters has outperformed GPT-3 on various reasoning tasks like language inference and commonsense reasoning. According to Google’s own research, PaLM solved 58% of GSM8K (Grade school math) problems with 8-shot prompting, surpassing GTP-3’s top score of 55% - despite the latter using external calculators and verifiers. (Source) 

The Transformer architecture, introduced in 2017, also marked a significant step for NLP’s development. This novel architecture uses self-attention mechanisms to improve model performance on tasks like machine translation. Bidirectional Encoder Representations from Transformers (BERT) is a prominent example for this. Developed by Google, it’s designed to enhance the NLP process by considering context on a sequential and non-sequential basis. By following this approach, models powered by BERT can grasp the meaning of words more accurately than any traditional NLP model. 

In areas like sentiment analysis, entity recognition, and text classification, Conventional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks have found success by capturing syntactic patterns and long-range dependencies. But since the advent of transformers, their usage has diminished significantly. 

How Does Natural Language Processing Work? 

Important Natural Language Processing Models

Noting various technical roots and practical applications of foundational NLP models reveal interesting comparisons between these complex architectures. Breaking down these innovations side-by-side offers valuable insights, especially in critical decisions where you'd need some models for specific tasks. Some key NLP models include

BERT (Bidirectional Encoder Representations from Transformers)

In NLP translation, BERT is a core model type you can't overlook. It revolutionized natural language processing by taking a bi-directional approach, where a sentence is encoded by looking at words left and right simultaneously. Unlike traditional unidirectional language models, BERT is an encoder-only model based on a multilayered transformer architecture tailored for understanding rather than generation. 

The BERT model houses two core innovations:

• Masked Language Model (MLM) - Some tokens are randomly masked during pretraining and the model uses bidirectional context to predict these masked tokens.
• Next Sentence Prediction (NSP) - BERT is also capable of understanding sentence relationships by predicting if two sentences are consecutive in the original text.

The model also features two unique variants:

• BERT_BASE - 12 layers & 110 million parameters
• BERT_LARGE - 24 layers & 340 million parameters

Generative Pre-trained Transformer 2 (GPT-2)

Unlike BERT, GPT-2 uses a transformer decoder-only architecture, predicting the next word in a sequence, following a unidirectional approach from left to right. It is trained on massive data (mostly internet corpora), enabling it to generate diverse and cohesive passages. As a result, you can consider this model for several use cases like creative writing and dialogue. 

Robustly Optimized BERT Approach (RoBERTa)

Similar to BERT, RoBERTa uses the former’s encoder architecture where the Next Sentence Prediction (NSP) task is removed entirely, focusing only on MLM for better token-level contextual representations. Pretraining for this model is typically done on larger datasets with bigger batch sizes and longer training times, improving accuracy over BERT on several natural language processing benchmarks. 

Extrapolation Language Network (XLNet)

Built on Transformer-XL architecture, XLnet can efficiently handle longer contexts through segment recurrence and relative positioning. It uses a permutation-based training method to capture bidirectional context while maintaining the autoregressive nature of language modeling—offering improvements in text generation and reasoning tasks. 

XLNet also offers a major advantage, especially over GPT and BERT through a combination of autoregressive modeling benefits and bidirectional context awareness. These qualities make it suitable for tasks that require long-term dependencies. 

Embeddings from Language Models (ELMo)

ELMo uses deep bidirectional LSTM networks that are trained to read sentences left-to-right and right-to-left. This model produces contextualized word embeddings instead of static embeddings, being able to read sentence context and capture syntax. 

ELMo embeddings are mostly used to improve performance of downstream tasks like sentiment analysis and named entity recognition, adding context sensitivity at the word level. 

Challenges Of Natural Language Processing

Though NLP has shown promising capabilities over the years in almost humanizing conversations, let’s not forget that even this technology has its own drawbacks. Here are well-recognized issues found within NLP models that are being consistently improved upon as we speak:

Language complexities like sarcasm, idioms, & context nuances

Computers struggle mightily with contextual subtleties like sarcasm or idiomatic expressions despite being able to dissect regular patterns in language. Sarcasm frequently involves mocking or lambasting someone or a viewpoint with nuanced tonal shading. Idioms possess figurative connotations starkly different from literal interpretations within a specific context.

Biases in training data impacting fairness and accuracy

Training natural language processing models on skewed datasets may produce detrimental outputs against specific demographics or cultures. Users must rectify such biases by gathering diverse data, implementing various bias detection methods, and regularly auditing model outputs for fairness. These preventative measures not only consume too much time, but may have significant computational resource demands.   These preventative measures not only consume too much time, but may have significant computational resource demands. 

Computational resources demands

Speaking of computational resource demands, it is widely known that training and deploying LLMs require substantial computational power and memory. It impacts overall feasibility of projects, also including high financial and environmental costs. 

Spelling/Grammatical errors

Even the simplest spelling or grammatical errors made can degrade overall model performance. Call it a “linguistic noise” if you may. These are errors that can easily be corrected, but when neglected, may result in a flawed output. Most natural language processing workflows typically use spell checking tools, text normalization, and tokenization to handle such errors and maintain accuracy in understanding input data. 

Problems with multilingualism

There is also the challenge of language-specific nuances users might struggle with when there’s a scarcity of standardized datasets for multiple languages. It's not just different languages, but the models need to correctly interpret sentiment across dialects, varying word connotations, and idiomatic expressions. This problem still remains an active research area, where we could expect data scientists to solve this in the near future.

Benefits Of Natural Language Processing 

NLP offers several benefits across various business and operational areas. Let’s dive into a few of them:

Automation and workflow simplification 

HR, IT, and sales teams can greatly benefit from NLP’s automation capabilities. NLP processes streamline repetitive context-based tasks via natural conversation interfaces and software provisioning ensuring efficiency always. Significant time savings and reduced manual efforts result from this, allowing users to focus on high-value tasks needing their urgent attention.

Enhanced documentation efficiency and accuracy

NLP accelerates documentation workflows by automating the extraction and processing of information from text-based documents like receipts, invoices, and other records. This reduces human errors and manual data entry requirements.

Enables customer support through chatbots

Natural language processing is and has always been the backbone towards the creation of intelligent chatbots and virtual assistants we use today. Through natural language, they respond to customer queries and execute tasks commanded by users. And the best part? They’re available 24/7 and will assist customers whenever they’re in need, reducing wait times and improving their experience. They also help lower operational costs by preventing the need for large human support teams. 

Simplifies sentiment analysis

NLP tools scrutinize vast troves of data, including surveys and social media posts, gauging customer sentiment as positive or negative. Companies can gain insights, tailor marketing plans, and address customer gripes through sentiment differentiation.

Best NLP Techniques

Natural language processing employs a variety of techniques for computers to understand, interpret, and generate human language. Some of them include:  

• Tokenization - Divides text into smaller units like words or sentences so computers can easily analyze language. 
• Stemming and Lemmatization - Stemming cuts down words to their base or root form, ignoring grammar (Example: “Playing” to “Play”). Lemmatization, on the other hand, does the same, but considers grammar and context (Example: “Better” to “Good”).
• Stop word removal - Filters out common words that carry little meaning in text analysis. By doing so, algorithms can focus on significant terms. Common words filtered out include “the,” “is,” “and.”
• Sentiment analysis - It gauges sentiments, automatically detecting the emotional tone behind text (such as positive, negative, or neutral) from sources like surveys, social media, or reviews. 
• Named Entity Recognition (NER) - NER focuses on converting unstructured data into structured data, by identifying named entities and categorizing them based on organizations, locations, or brands. 

NLP Use Cases

As organizations seek to extract value from the vast amounts of unstructured data generated every day, the practical applications of natural language processing have shown diverse use cases across several industries. Let’s look at some instances of how this technology is being used to address real-world challenges and drive innovation:

Machine translation 

Machine translation systems fueled by NLP decipher text or speech from one language into another by analyzing  grammatical structures and idiomatic expressions. Not only must the translated output be linguistically accurate, but the intended meaning of the output must also be preserved. This is vital for cross-communication measures, especially when translation systems have to make information accessible to a diverse, multilingual audience. 

Example - Google Translate uses NLP to translate hundreds of languages in real time, effectively bringing down language barriers for users worldwide to learn and communicate properly.

Speech recognition

At the heart of speech recognition technologies is none other than NLP where spoken language, despite variations in accents, intonations, and speed, is easily transformed into precise textual data. What powers this conversion includes sophisticated acoustic modeling and language modeling that transcribes human speech in real time with higher accuracy.

Example - For speech recognition, look no further than Apple’s Siri. This practical and well-known example uses NLP to convert spoken language into text, enabling hands-free interactions such as making calls or controlling smart home devices. 

Healthcare 

Here's an interesting titbit. About 80% of healthcare data today remains unstructured. (Source) Whether its clinical notes, EHRs, or medical research notes, natural language processing has untapped potential here.

Healthcare providers make accurate decisions swiftly by extracting pertinent medical diagnoses, thereby administering befitting treatment plans effectively. NLP enables providers to offer personalized patient care while simplifying coding processes and billing.

Example - By analyzing patient records, NLP identifies potential adverse drug reactions by analyzing patient records and offers precision medicine for personalized therapies and treatment plans. 

Finance

Financial entities mostly harness natural language processing for scrutinizing massive amounts of unstructured text data such as earnings reports and regulatory filings. Computers generate insights that aid identification of compliance risks and shady dealings while automating processing of complex financial paperwork. Finance professionals and institutions can enhance decision-making by leveraging NLP solutions effectively, especially in an industry where precision matters greatly.

Example - It is commonly used for sentiment analysis on news or social media platforms to gauge investor emotions. With the data obtained, firms can anticipate market movements and alter their strategies accordingly.

Spam filtering

Malicious spam emails may often land in your inbox frequently with nefarious intent beneath innocuous subject lines. Spam filtering becomes feasible through natural language processing that scrutinizes email content and messages, identifying suspicious patterns or keywords. Making such a distinction between legitimate content prevents various cyber threats that may severely compromise an organization's overall security or its reputation.

Example - Gmail’s spam filter, powered by NLP, detects unwanted emails and isolates them as spam messages. 

Wrapping Up

Natural language processing is at the forefront of human-machine interaction, enabling this fluid communication and facilitating the realization of language data’s full potential. On top of that, there is no telling what we will see in terms of technology further down the line! From speaking hundreds of languages in a human-like manner to even understanding and responding humanly to those with disabilities, there’s so much NLP could do.  From speaking hundreds of languages in a human-like manner to even understanding and responding humanly to those with disabilities, there’s so much NLP could do. 

Forward-thinking businesses recognize the potential of this innovation and are adopting it for smarter automation and to offer superior customer experiences. And if you’re one of those businesses, Tredence emerges as your ideal partner in NLP solutions and expertise. With a collaborative approach and through our cutting-edge solutions, we aim to deliver measurable value from your language data assets. Partner with us today and shape the future of human-computer interaction!

FAQs

1] Is NLP only useful for large enterprises?

Natural language processing isn’t just for large enterprises. Startups and SMEs alike can leverage this tech and benefit enormously from it. Optimizing customer service or streamlining business processes suits everyone regardless of size in fairly diverse corporate settings.

2] How has NLP improved with transformer-based models?

NLP has greatly advanced transformer-based models used in today's commercial software and has improved capabilities in machine translation, text summarization, and sentiment analysis. They are adept at understanding the context and subtle relationships between text. 

3] How is NLP different from regular text analysis?

Regular text analysis often relies on simple, rule-based systems, while natural language processing takes advantage of AI and computational linguistics to understand subtleties around meaning in human language.

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Editorial Team
Tredence