With newer and more efficient chatbots, machine translation tools, and other effective offerings, NLP is currently being leveraged to help each business and individual. With the NLP market projected to grow from USD 24.10 billion in 2023 to USD 112.28 billion by 2030, the potential utilization can be easily predicted and estimated. People with an interest in the field must remain updated about current proceedings. Helping you in the endeavor, explore the top NLP models and examples. 

Understanding Language Models in NLP

Natural Language Processing is a field in Artificial Intelligence that bridges the communication between humans and machines. Enabling computers to understand and even predict the human way of talking, it can both interpret and generate human language. The language model assists NLP in the task. 

Language models are the tools that contribute to NLP to predict the next word or a specific pattern or sequence of words. They recognize the ‘valid’ word to complete the sentence without considering its grammatical accuracy to mimic the human method of information transfer (the advanced versions do consider grammatical accuracy as well). 

The language models are trained on large volumes of data that allow precision depending on the context. Common examples of NLP can be seen as suggested words when writing on Google Docs, phone, email, and others.

Types of Natural Language models

The natural language models can be divided into two categories: statistical models and neural language models. 

Statistical Language Models 

Using statistical patterns, the model relies on calculating ‘n-gram’ probabilities. ‘n’ is greater than zero. Hence, the predictions will be a phrase of two words or a combination of three words or more. Markov's assumptions are used to find the final word. It states that the probability of correct word combinations depends on the present or previous words and not the past or the words that came before them. 

Among the multitude of n-gram words predicted by the statistical model, the likelihood will be calculated by counting the number of times each word combination appears and then dividing it by the number of times the previous word appears. There is a drawback associated with n-gram models, which lack consideration of the long-term context of words in a sequence.  

Neural Language Models 

The word prediction here is based on the neural networks. Neural language models use two common neural network architectures: Recurrent Neural Networks and Transformer Networks. RNN’s efficiency can be attributed to the memorization of previous outputs present in the hidden layer of the network. 

Transformers, on the other hand, are capable of processing entire sequences at once, making them fast and efficient. The encoder-decoder architecture and attention and self-attention mechanisms are responsible for its characteristics. 

The neural language model method is better than the statistical language model as it considers the language structure and can handle vocabulary. The neural network model can also deal with rare or unknown words through distributed representations. 

Examples of NLP Models

Machine Translation

NLP models are capable of machine translation, the process encompassing translation between different languages. The most common example is Google Translate. These are essential for removing communication barriers and allowing people to exchange ideas among the larger population. Machine translation tasks are more commonly performed through supervised learning on task-specific datasets.

However, research has also shown the action can take place without explicit supervision on training the dataset on WebText. The new research is expected to contribute to the zero-shot task transfer technique in text processing.


Optical Character Recognition is the method to convert images into text seamlessly. The services expand both through document scanning and taking pictures. The prime contribution is seen in digitalization and easy processing of the data. Language models contribute here by correcting errors, recognizing unreadable texts through prediction, and offering a contextual understanding of incomprehensible information. It also normalizes the text and contributes by summarization, translation, and information extraction. 

Sentiment Analysis

Also known as opinion mining, sentiment analysis is concerned with the identification, extraction, and analysis of opinions, sentiments, attitudes, and emotions in the given data. NLP contributes to sentiment analysis through feature extraction, pre-trained embedding through BERT or GPT, sentiment classification, and domain adaptation. 

XLNet, based on the autoregressive pre-training method, has recently become the better sentiment analyzer. It overcomes the limitations of BERT NLP based on the same method and outperforms multiple of BERT’s tasks, including sentiment analysis. 


BERT NLP, or Bidirectly Encoder Representations from Transformers Natural Language Processing, is a new language representation model created in 2018. It stands out from its counterparts due to the property of contextualizing from both the left and right sides of each layer. It also has the characteristic ease of fine-tuning through one additional output layer. It is capable of processing 11 NLP tasks, among other capabilities.  

The massive pre-training dataset further enhanced its capabilities. Overall, BERT NLP is considered to be conceptually simple and empirically powerful. Further, one of its key benefits is that there is no requirement for significant architecture changes for application to specific NLP tasks. 


One of the most used and important parts of current businesses nowadays, chatbots, is a live example of NLP. They are designed to carry out human-like conversations and comprise three main components: Natural Language Understanding, dialog management, and Natural Language Generation. BERT, XLNet, and ALBERT language models can assist in an exquisite chatbot experience. 


Parsing is another NLP task that analyzes syntactic structure of the sentence. Here, NLP understands the grammatical relationships and classifies the words on the grammatical basis, such as nouns, adjectives, clauses, and verbs. NLP contributes to parsing through tokenization and part-of-speech tagging (referred to as classification), provides formal grammatical rules and structures, and uses statistical models to improve parsing accuracy. 

Text Generation

It is the core task in NLP utilized in previously mentioned examples as well. The purpose is to generate coherent and contextually relevant text based on the input of varying emotions, sentiments, opinions, and types. The language model, generative adversarial networks, and sequence-to-sequence models are used for text generation

The applications, as stated, are seen in chatbots, machine translation, storytelling, content generation, summarization, and other tasks. NLP contributes to language understanding, while language models ensure probability modeling for perfect construction, fine-tuning, and adaptation. 

Text Summarization

It is the NLP function that enhances the readability of the data and increases productivity. By eliminating the requirement to read relatively larger texts, it is capable of summarization in small, easy-to-read, and comprehensible points. The text summarization is performed through two approaches: extractive and abstractive. 

While extractive summarization includes original text and phrases to form a summary, the abstractive approach ensures the same interpretation through newly constructed sentences. NLP techniques like named entity recognition, part-of-speech tagging, syntactic parsing, and tokenization contribute to the action. Further, Transformers are generally employed to understand text data patterns and relationships. 


XLNet utilizes bidirectional context modeling for capturing the dependencies between the words in both directions in a sentence. Capable of overcoming the BERT limitations, it has effectively been inspired by Transformer-XL to capture long-range dependencies into pretraining processes. With state-of-the-art results on 18 tasks, XLNet is considered a versatile model for numerous NLP tasks. The common examples of tasks include natural language inference, document ranking, question answering, and sentiment analysis. 

Text Classification

The NLP task involves categorizing the text documents into predefined classes or categories depending on the content. It is responsible for carrying out NLP tasks such as sentiment analysis, topic classification, intent recognition, and spam detection. It includes methods like TF-IDF, bag-of-words, and word embeddings for representing text data in a numerical format suitable for classification algorithms. 

The text classification tasks are generally performed using naive Bayes, Support Vector Machines (SVM), logistic regression, deep learning models, and others. The algorithm requires pretraining. The text classification function of NLP is essential for analyzing large volumes of text data and enabling organizations to make informed decisions and derive insights. 


The Unigram model is a foundational concept in Natural Language Processing (NLP) that is crucial in various linguistic and computational tasks. It's a type of probabilistic language model used to predict the likelihood of a sequence of words occurring in a text. The model operates on the principle of simplification, where each word in a sequence is considered independently of its adjacent words. This simplistic approach forms the basis for more complex models and is instrumental in understanding the building blocks of NLP.

What are Pretrained NLP Models?

Pretrained models are deep learning models with previous exposure to huge databases before being assigned a specific task. They are trained on general language understanding tasks, which include text generation or language modeling. After pretraining, the NLP models are fine-tuned to perform specific downstream tasks, which can be sentiment analysis, text classification, or named entity recognition. 

The pre-trained models allow knowledge transfer and utilization, thus contributing to efficient resource use and benefit NLP tasks. Some of the popular pre-trained NLP models have been discussed as examples. The examples include GPT, BERT, and XLNet

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Get Started with Natural Language Processing

Artificial Intelligence has taken over the world. With multiple examples of AI and NLP surrounding us, mastering the art holds numerous prospects for career advancements. Candidates, regardless of their field, now have the opportunity to ace their careers. 

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1. What are the 7 levels of NLP?

The seven processing levels of NLP involve phonology, morphology, lexicon, syntactic, semantic, speech, and pragmatic. 

2. What is an example of a Natural Language Model?

Among the varying types of Natural Language Models, the common examples are GPT or Generative Pretrained Transformers, BERT NLP or Bidirectional Encoder Representations from Transformers, and others. 

3. What is the classification of NLP Models?

NLP models can be classified into multiple categories, such as rule-based models, statistical, pre-trained, neural networks, hybrid models, and others. 

4. What is the difference between NLP and AI?

The NLP is a subset of AI. AI encompasses the development of machines or computer systems that can perform tasks that typically require human intelligence. On the other hand, NLP deals specifically with understanding, interpreting, and generating human language.

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