Applications of Bigrams

Bigram applications in natural language processing (NLP) and text analysis are :

• Language modeling: Language models are statistical models that calculate the likelihood of a sequence of words appearing together in a particular language. Bigrams estimates the probability of one word following another. These models are applied in machine translation, speech recognition among other activities.
• Text prediction: Text completion relies on bigrams to guess what comes next given what came before it. You can predict reasonably well the following word by studying the frequency of various bigrams occurring within your data set only. This is used by autocomplete features on search engines or messaging apps.
• Information retrieval: Bigrams indexation during information retrieval systems design allows for faster searching through documents because they represent more closely related pairs than single terms do thus increasing precision as well as recall rates when retrieving searched items from large collections like those found online.
• Text classification: Sentiment analysis, spam detection, and topic categorization can all benefit from treating bi-grams as classification attributes. In sentiment analysis for example considering two-word combinations gives more context about the expressed opinion thus helping classifiers make accurate decisions whether something is positive or negative.
• Named Entity Recognition (NER): In NER systems, bigrams are used for the identification of named entities like person names, locations and organizations in text. NER models performance can be enhanced by this through capturing patterns of words that frequently appear together within such entities.
• Spelling Correction: Bigrams may also be employed in spelling correction systems to propose corrections for misspelled words. A system could suggest likely alternatives by comparing a misspelt word’s bigram with those from correctly spelled words found on dictionary.

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.