Introduction NLP Models 

NLP stands for Natural Language processing. NLP models arise with the great or big advancements in the Artificial Intelligence field. These models run by advanced and complicated algorithms with massive datasets. NLP models have changed or shifted how we interact and understand human texts, sentiments, questions- answering etc. NLP is Natural Language Processing, combining the power of linguistics and computer science, which helps to study the rules and structure of the language and create an intelligence system capable enough to understand, analyze, analyze and extract meaning from the text.  

Types of NLP (Natural Language Processing) models

There are 7 types of models, each designed to address specific language processing tasks. 

  1. Rule Based Models 
  2. Statistical Models
  3. Neural Network Models 
  4. World Embedding Models 
  5. Pre- Trained Language Models 
  6. Domain Specific Model 
  7. Sequence to Sequence Model

Rule Based Models 

are built on a set of rules that describe how words can be joined. They built language rules and patterns to process and analyze material. These models include manually crafted rules to perform various tasks. Rule-based models are one of the oldest NLP methods 1st stage is related to rule creation; next is rule application. After that, rule processing, and last, rule refinement. These models are less flexible and require expert Knowledge to create and maintain the rules. 

Statistical Knowledge 

This is related to using statistical Knowledge or technique to analyze language. These models can also predict the next word in the sequence from annotated training data; they utilize machine learning algorithms to discover patterns and correlations. The statistical model helps to suggest the auto- completes, detect and correct spelling errors, caption images, summarize text, recognize speech etc. 

Neutral Network Models 

It can process and understand language. Neutral language enables the computer to perform the NLP process text or documents can be processed, information extracted, and the meaning of data determined. 7 types of Neutral Networks are:

  • Feed forwards.
  • Neural nests.
  • Multi-layered perceptron neural nests.
  • Convolution neural nets.
  • Radial-based function neural nets.
  • Sequence models.
  • Modular neural networks.  

Word Embedding Models 

This model helps to capture the semantic and syntactic context to understand how similar and dissimilar an article, blog etc. Word embedding executes language modelling and feature extraction-based techniques to map a word to a vector of real numbers. Some popular Word Embedding techniques include Word2Vec, Glove embedding, Fastext, BERT, ELMO, etc. This is used in various NLP tasks such as text classification, semantic similarity and named entity recognition.  

Sequence-to-Sequence Models 

These models are useful in machine translation, text summarization-summarization, and conversational agents. This model is a type of encoder and decoder that takes a sequence of items (words, letters, time series etc.) and outputs another sequence of items.  

Pre-trained Language Models 

These models are deep learning models such as GPT(Generative Pre-Trained Transformer) and BERT(Bid directional encoder representations from transformers) Roberta, ELMo etc. They can do a wide range of tasks such as Text classification, Name Entity Recognition, Sentiment analysis and Question- answering.  

Best NLP models

There are some best NLP models are 

  1. BERT (Bidirectional encoder representations from transformers)
  2. GPT(Generative Pre-Trained Transformer)
  3. Transformer 
  4. XLNet
  5. Roberta(Robustly OptimizedOptimized BERT approach)
  6. Electra (Efficiently Learning an Encoder that Classifies Token Replacements Accurately)


Every model type has its pros and cons, its strengths and weaknesses, and the best model will depend on the work, the data at hand, and the required performance. The best model may differ according to the specific tasks, available resources and the quantity of training data. Technology will advance daily, and the world is changing and growing so fast; every single day, NLP research and innovation are better than yesterday.