Language Modeling: Types and Importance in Artificial Intelligence

What is Language Modeling

Language modelling or LM is the use of various statistical techniques to determine the probability of a sequence of words in a sentence. The purpose of language models is to analyse the bodies of text data to provide a basis for their word predictions. Companies like AI Seed use this language modelling in applications for Natural Language Processing (NLP). Some of the most common applications include machine translation and question answering. 

Types of Language Modeling

  1. Statistical Language Modeling

As its name suggests, statistical language modelling means the development of probability models that predict the next work on the sequence given in the preceding words. Based on the examples of text, the language model determines and predicts the probability of word occurrence. The simple model works on the context of a short sequence of words, whereas complex models work on entire sentences or paragraphs. However, commonly speaking, language models work on short word sequences mostly. A language model can be developed and applied as a standalone application, such as generating new words that appear from previous ones. Language modelling serves as the root problem for a range of natural processing tasks.


The practical use of language models is in front-end or back-end of a more sophisticated model that requires an understanding of language. An excellent example of language modelling is in speech recognition programs developed by companies like AI Seed, where audio data is used as an input to the model. The output required a language model to interpret the voice signals. This model then analyses these signals to predict the new word in the context of the words already recognised. Similarly, language modelling is used to determine and generate text in various natural language processing tasks. Some of the most common ones include:

  • Image Captioning
  • Machine Translation
  • Spelling correction
  • Optical character and handwriting recognition
  • Text summarisation and many others


  1. Neural Language Models

Neural language modeling has become popular in recent years and is anticipated to become the preferred approach for machine learning. The use of neural networks in machine language models is called Neural Language Modeling or NLM for short. Neural network-based models have garnered better results than traditional methods both in standalone and incorporation with larger models. These models are most widely preferred to handle complex tasks like speech recognition and machine learning. The key reason companies like AI Seed adopt this model is the model’s ability to generalise. This model assumes word embedding in a real-valued vector setting representing each word in a projected vector space.The representation of words based on their use allows the model to give similar representation to words having the same meaning. The generalising nature of this model makes it more efficient than the statistical model. Furthermore, the distributed representation ability allows the word embedding to be represented better with the size of the vocabulary. The three properties based on which a neural model works are:


  • Association of words in the vocabulary with a distributed feature vector
  • Express joint probability functions of word sequence for feature vectors
  • Learn the feature vector and parameters of probability function simultaneously

Importance of Language Modeling

Language modelling is a crucial part of modern NLP applications and is why machines understand qualitative information. In one way or another, each model turns qualitative information in quantitative for better prediction and understanding. This allows the communication of people with machines as they do with each other to a limited extent. Companies like AI Seed develop programs for a variety of industries like tech, finance, healthcare, and transportation, legal, military, and government that use machine language modelling. Today, it is likely that a person interacts with machines in some way, whether it is Google search, auto-correct function, or voice assistant.



Language modeling applications include the following:

  • Speech recognition where machines process speech or audio data. Typical examples are Alexa and Siri.
  • Translation of one machine language to others. Examples include Google Translate and Microsoft Translate.
  • Characterisation and markup of different words by grammatical characteristics for parts-of-speech recognition.
  • Recognise sentiment behind a word or phrase to understand opinions and attitudes behind that text.

Analysis of any string of data or sentence that converts to formal grammar and syntax rules.

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