Text Representation Using Bag Of n-grams: NLP Tutorial For Beginners - S2 E5

TL;DR

Bag of N-grams model captures the order of words in text by using pairs or groups of words instead of individual words.

Transcript

We looked at bag of words model in our last video, and what we saw was to classify news articles. We created this vocabulary of individual words or tokens, and then we counted words in each of these articles. Now this approach works fine. But if you think about it, we are missing an important point here which is, in a language the order of words is... Read More

Key Insights

• 👜 The bag of n-grams model captures the order of words in text, which is important for understanding meaning.
• 🙅 N-grams can be used to improve the representation of text and enhance the performance of machine learning models.
• 👜 The bag of words model is a special case of the bag of n-grams model, where n is one.
• 😀 The dimensionality and sparsity of the model increase as the value of n in n-grams increases.
• 😑 Pre-processing techniques like stop word removal and lemmatization can further improve the performance of the bag of n-grams model.
• 🏛️ Class imbalance can be addressed by undersampling or other techniques in machine learning.
• 😑 Pre-processing text by removing stop words and using lemmatization can improve the performance of text classification models.

Questions & Answers

Q: Why is word order important in language?

Word order is important in language because it determines the meaning of a sentence. Changing the order of words can completely change the meaning of the sentence.

Q: How does the bag of n-grams model capture word order?

The bag of n-grams model captures word order by counting pairs or groups of words instead of individual words. This allows for a more meaningful representation of text.

Q: What is the difference between bi-grams and tri-grams?

Bi-grams capture pairs of words, while tri-grams capture groups of three words. The generic term for this approach is n-grams, where n can be any number.

Q: What are the limitations of the bag of n-grams model?

As the value of n increases, the dimensionality and sparsity of the model increase, leading to more computation and memory issues. Additionally, the model does not address the out of vocabulary problem.

Key Insights:

• The bag of n-grams model captures the order of words in text, which is important for understanding meaning.
• N-grams can be used to improve the representation of text and enhance the performance of machine learning models.
• The bag of words model is a special case of the bag of n-grams model, where n is one.
• The dimensionality and sparsity of the model increase as the value of n in n-grams increases.
• Pre-processing techniques like stop word removal and lemmatization can further improve the performance of the bag of n-grams model.
• Class imbalance can be addressed by undersampling or other techniques in machine learning.
• Pre-processing text by removing stop words and using lemmatization can improve the performance of text classification models.
• Bag of n-grams can be combined with other text representation techniques like TF-IDF for better performance.

Summary & Key Takeaways

• In the bag of words model, individual words are counted, but the order of words is not captured, while in the bag of n-grams model, pairs or groups of words are counted to capture word order.

• Bi-grams and tri-grams are examples of n-grams, where pairs and groups of two and three words are used, respectively.

• The bag of words model is a special case of the bag of n-grams model, where n is one.

• The bag of n-grams model can improve the representation of text and can be used in machine learning models.