The Bag-of-n-grams model is a statistical language model for text prediction that is used in the text analytics industry. This model predicts the next word in a sequence based on the previous n words. The n in bag-of-n-grams refers to the number of words that are considered when making predictions.
How is Bag-of-n-grams model different from other models?
Bag-of-n-grams is different from other models because it takes into account the order of words in a sequence. Other models, such as bag-of-words, do not consider the order of words and instead treat each word as a separate entity.
What are some applications of Bag-of-n-grams model?
Bag-of-n-grams model can be used for a variety of tasks such as spell checker, text completion, and next word prediction.
Tools of Bag-of-n-grams model
There are a few different ways to create a bag-of-n-grams model. One way is to use the NLTK library in Python. Another way is to use the Gensim library.
The NLTK library is a popular tool for natural language processing that includes a bag-of-n-grams module. The Gensim library is another toolkit for natural language processing that has a Word2Vec implementation, which can be used to create a bag-of-n-grams model.
Both of these libraries are open source and free to use.
Advantages and Disadvantages
One advantage of the bag-of-n-grams model is that it is relatively simple to implement. Additionally, this model can be used with any size of text data.
A disadvantage of the bag-of-n-grams model is that it does not take into account the context of words. Additionally, this model requires a large amount of training data in order to make predictions accurately.
Bag-of-n-grams vs other models
There are a few different types of models that can be used for text prediction. The bag-of-n-grams model is one type of model. Other types of models include the bag-of-words model and the n-gram model.
The bag-of-words model is a statistical language model that predicts the next word in a sequence based on the previous words in the sequence. The n-gram model is a statistical language model that predicts the next word in a sequence based on the previous n words in the sequence.
Both of these models are similar to the bag-of-n-grams model. However, the bag-of-n-grams model is different because it takes into account the order of words in a sequence. Additionally, the bag-of-n-grams model can be used with any size of text data.