Generating Text Using N-gram Models and Neural Language Models

Natural Language Processing (NLP) has revolutionized various applications such as machine translation, sentiment analysis, and chatbots. An essential task in NLP is generating coherent and meaningful text. In this article, we will discuss two popular methods for generating text: n-gram models and neural language models.

N-gram Models

N-gram models are language models that predict the next word in a sequence given the previous n-1 words. These models are based on the assumption that the likelihood of a word occurring depends only on the previous n-1 words. For example, in a trigram model, the probability of the next word is calculated based on the two preceding words.

To build an n-gram model, we need a corpus of text as training data. This corpus is divided into sequences of words, and the frequency of each n-gram sequence is recorded. To generate text using the model, we start with an initial n-1 word sequence and randomly select the next word based on the probability distribution of the n-grams. This process is repeated to generate subsequent words, resulting in a coherent text.

N-gram models are relatively easy to implement and provide a simplistic representation of language. However, they have limitations. Since they only consider a fixed number of preceding words, they may not capture long-range dependencies in the text. Additionally, they are prone to data sparsity issues when encountering unseen n-grams.

Neural Language Models

Neural language models aim to address the limitations of n-gram models by employing neural networks to generate text. These models learn the probability distribution over the next word given the context of the preceding words. Unlike n-gram models, neural language models can capture complex relationships and adapt to various contexts.

One popular neural language model is the Recurrent Neural Network (RNN). RNNs are designed to process sequential data and are well-suited for generating text. The RNN maintains a hidden state that persists information across time steps, allowing it to consider the entire history of the input sequence. By training the RNN on a corpus of text, it learns to generate coherent and contextually appropriate text.

Another advanced neural language model is the Transformer model. Transformers employ self-attention mechanisms, enabling them to capture long-range dependencies efficiently. This model has gained popularity due to its ability to generate high-quality text and its parallelization capabilities, enabling faster training and generation.

Neural language models require substantial computational resources for training due to their complex architectures. However, they offer significant advantages over traditional n-gram models in terms of generating more accurate and fluent text.


Generating text is an essential task in NLP, and both n-gram models and neural language models provide valuable approaches. N-gram models offer simplicity and ease of implementation but may lack in capturing long-range dependencies. On the other hand, neural language models, such as RNNs and Transformers, excel at generating coherent and contextually appropriate text but require more computational resources. Choosing the appropriate method depends on the specific requirements of the task.

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