Top Language Models in NLP: Types, Features and Examples

Natural Language Processing (NLP) language modeling is in vogue these days. They help applications execute multiple tasks like speech recognition, speech-to-text conversion, machine translation, sentiment analysis, and much more. These models form the core of NLP.  

As companies continue to invest in artificial intelligence (AI) and related technologies, NLP language models will continue to grow in significance. Research suggests that the market is expected to grow from around $20 billion in 2021 to $26 billion by the end of 2022. Language modeling Artificial intelligence solutions have been helping businesses make profiles. 

This article explains what is language modeling, language models in NLP, how they work, their types, and examples.  

What is Language Modeling in NLP?

Have you seen the Smart Reply and Smart Compose features in Gmail? They help you quickly reply to emails or complete the text you are typing. This is how language models are used in NLP, a branch of Edge AIDo you know this all is possible with language modeling? 

Language modeling refers to different probabilistic and statistical methods used to determine the chances of a given series of words occurring in a text. These models in language modeling analyze text and provide predictions as to which words might come next.  

Large language models (LLMs) are an integral part of language modeling. They are widely known for their advanced language capabilities. These advanced language models employ modern technology such as AI to handle and process billions of data. OpenAI’s GPT-3 and Google’s Palm 2 are a part of LLM and they can deal with mounds of training data parameters. Also, they can generate textual output in response to the information to find meaning from within the data.  

NLP applications and language modeling use these models as a basis for word predictions, especially ones that produce text as output. Question-answering and machine translation applications are common examples of language modeling.  

How does NLP Modeling work?

NLP models analyze text to give the word probability. This interpretation through language modeling first entails feeding the text data to an algorithm that specifies rules for context in natural language. Language modeling then uses these rules and applies them to language tasks to create new sentences or give accurate predictions.   

In other words, the model imbibes a language’s fundamental characteristics and features and uses them to understand new sentences or phrases. The same is used to make word/phrase predictions in NLP models. 

Various probabilistic approaches are employed in training a language model. However, the approach used is determined by the purpose of the language model. The difference between the approaches in language modeling generally revolves around the amount of text analyzed and the type of math used in top NLP techniques.

Let’s look at the types of language models.  

Types of Language Models in NLP

There are essentially two types of language models.  

  1. Statistical Language Models  

It is one of the widely used types of language models in NLP. Statistical language models involve creating probabilistic models or NLP models able to identify (using prediction) the next word in a phrase or sequence based on the words preceding it.   

Quite a few statistical models exist today. Let’s look at some of the NLP modeling types of these briefly.  

N-Gram – It is an easy-to-use approach in language modeling. N-Gram creates a probability distribution for a sequence of ‘n.’ The ‘n’ in this NLP model defines the gram’s size or a series of words being assigned a probability and can be any number. So, suppose if n=3, a gram may look like “Are you there.” Think of ‘n’ as the amount of context the model has to consider. There are various types of N-grams like unigrams, bigrams, trigrams, etc.  

Unigram – It is often considered the most uncomplicated kind of language model. This language model in NLP does not assume any conditioning context while executing calculations. Instead, it analyzes each phrase, term, or word independently. Unigrams are commonly used to perform language processing tasks like information retrieval. It serves as the basis for the query likelihood model in NLP modeling and NLP models which is more specific and uses information retrieval to evaluate a cohort of documents and match the most appropriate one to a particular query.  

  1. More Models

Bidirectional – Whereas N-gram models analyze text backward only, bidirectional models analyze text both ways – forward and backward. It uses all the words in a text body to predict any word or sentence in a body of text. Thus, bidirectional examination of the text in NLP modeling increases accuracy manifold. It is often employed in speech generation applications and machine learning. Google technology also uses a bidirectional model for search queries.  

Exponential Language modeling in NLP uses a combination of feature functions and n-grams using an equation to evaluate text. The exponential model lays down the features and parameters of desired results. It is based on the principle of entropy, which says that probability distribution with the highest entropy is the best option. These language models in NLP maximize cross-entropy, thus limiting the number of assumptions one can make. Hence, it offers high accuracy.  

Continuous Space – It shows words as a non-linear combination of weights in a neural network. The weight-assigning process is called word embedding. This type of language modeling in NLP. It is a very useful model where large datasets with unique words involve. Too many words (rarely used) often cause problems for linear models like n-gram.   

  1. Neural Language Models  

These models are considered an advanced way of executing NLP tasks based on neural networks. Neural language models come without the shortcomings of classical models like n-grams. They use in complex operations like machine translation and speech recognition.   

Language is an ever-evolving phenomenon. New words enter the lexicon, and old words lose favor with people regularly. Thus, a more complex model is better suited to perform NLP tasks. Moreover, language models must be able to identify dependencies, for instance, by knowing how words are derived from various languages.  

Examples of Language Models in NLP

Here are some common examples and use cases of language models in natural language processing. These NLP models will help you understand what is language modeling. 

  • Speech recognition – The best examples of speech recognition software are voice assistants like Siri and Alexa. This NLP model can process human audio speech.  
  • Machine translation this particular NLP model entails translating one language into another. Microsoft Translator and Google Translate are two leading examples of how NLP models aid in language translation.  
  • Text suggestions – As stated earlier, Gmail uses text suggestions through its Smart Compose feature using NLP models. These suggestions help you write text in emails and long documents.  
  • Sentiment analysis This NLP model helps identify the sentiment of a phrase or the opinion and attitude represented by a body of text. Many companies now employ sentiment analysis to gauge customer and employee feedback. The NLP model in sentiment analysis enables them to tailor their products and services accordingly. Google’s NLP tool, Bidirectional Encoder Representations from Transformers (BERT), is an example of a sentiment analysis tool.  
  • Parsing This language model in NLP involves the examination of words or sentences that follow grammar and syntax rules. Spell-check applications and the autocorrect feature on your smartphone use parsing and language modeling.  
  • Optical character recognition This language model in NLP is the mechanical or electronic conversion of images of text into machine-encoded text. The image can be a document photo, a scanned document, or any image with text in it.   


Language modeling is fast becoming a critical part of our digital lives. Language models have become deeply embedded in our lives, from using Alexa to play our Spotify playlist to using autocorrect on our smartphones for better communication.   

The power of language models, NLP, and artificial intelligence will only grow in the coming years. The NLP language models are transformative.  

Are you looking for help with language models? Contact us at [email protected]. Our certified AI experts will make a language model for you, capable of executing simple and complex NLP tasks.  



Language modeling plays a crucial role in various NLP tasks by predicting the likelihood of word sequences in a given context. It enables applications like speech recognition, machine translation, and sentiment analysis to generate accurate outputs and enhance user experience. Through probabilistic and statistical methods, language models help in understanding and processing natural language data efficiently. 

Large language models, such as OpenAI’s GPT-3 and Google’s Palm 2, represent a significant advancement in NLP. These models utilize advanced technologies like AI to process massive amounts of data and generate coherent textual outputs. By understanding context and predicting word sequences, LLMs enable more sophisticated NLP applications, ranging from question-answering systems to text generation. 

Language modeling in NLP involves analyzing text data and predicting the probability of the next word or sequence of words based on the context. This process begins by feeding text data into an algorithm that learns the rules and patterns of natural language. Using probabilistic approaches, the model generates predictions, allowing for tasks like text generation and word prediction in various NLP applications. 

In NLP, there are primarily two types of language models: statistical language models and neural language models. Statistical models, such as N-grams, rely on probability distributions to predict the next word based on preceding words. On the other hand, neural language models leverage neural networks to capture complex linguistic patterns and dependencies, enabling more accurate predictions and handling of contextual information. 

Language models in NLP drive a variety of applications, including speech recognition (e.g., Siri, Alexa), machine translation (e.g., Google Translate), text suggestions (e.g., Gmail’s Smart Compose), sentiment analysis (e.g., Google’s BERT), parsing, and optical character recognition. These applications leverage language modeling to understand, interpret, and generate human language, enhancing user interactions and productivity. 

Despite their effectiveness, language models face challenges such as handling out-of-vocabulary words, addressing bias in training data, and mitigating the risk of generating inappropriate or misleading content. Additionally, scaling language models to handle large datasets and optimizing computational resources remain areas of ongoing research and development in NLP. 

Businesses can harness language models to improve customer engagement, streamline operations, and gain insights from textual data. By integrating NLP-powered applications like chatbots, sentiment analysis tools, and language translation services, organizations can enhance communication, automate tasks, and make data-driven decisions to stay competitive in today’s digital landscape. 

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