Bigrams in NLP
Why are bigrams important?
Bigrams help us to understand some of the sequential structures of languages and can reveal relationships between words within a given context. They are used in sentiment analysis, part-of-speech tagging (POS), named entity recognition (NER), information retrieval (IR) etc.
How can I generate bigrams using NLTK?
To generate bigrams using NLTK library, you need to follow two steps :Tokenize your text into words (or sentences) using word_tokenize() function.Then call bigrams() method on created tokens.
Generate bigrams with NLTK
Bigrams, or pairs of consecutive words, are an essential concept in natural language processing (NLP) and computational linguistics. Their utility spans various applications, from enhancing machine learning models to improving language understanding in AI systems. In this article, we are going to learn how bigrams are generated using NLTK library.
Table of Content
- What are Bigrams?
- How Bigrams are generated?
- Generating Bigrams using NLTK
- Applications of Bigrams
- FAQs on Bigrams in NLP