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(Created page with "{{see also|Machine learning terms}} ==Bigram in Machine Learning== A '''bigram''' is a fundamental concept in the field of natural language processing (NLP), a subfield of machine learning. Bigrams are pairs of consecutive words in a given text or sequence of words. They play a vital role in various NLP tasks, such as language modeling, text classification, and sentiment analysis, by capturing the contextual information of words in a language. ===Definition and...") |
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[[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]] | [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]] | ||
==Introduction== | |||
In the field of [[machine learning]] and [[natural language processing]] (NLP), a '''bigram''' is a fundamental concept that refers to a pair of consecutive words or tokens in a given sequence of text. Bigrams are used in various NLP and machine learning tasks, such as language modeling, feature extraction, and pattern recognition, among others. This article will discuss the significance of bigrams in machine learning, their applications, and how they contribute to enhancing the performance of different algorithms. | |||
==Bigram Representation== | |||
A bigram is created by dividing a text sequence into consecutive pairs of words or tokens. The primary goal of using bigrams is to capture contextual relationships between adjacent words, which can be helpful in improving the performance of various machine learning models. | |||
===Tokenization=== | |||
Before forming bigrams, the raw text must be preprocessed and tokenized. [[Tokenization]] is the process of breaking down a text into individual words or tokens. This is typically done using various NLP techniques, such as whitespace tokenization, rule-based tokenization, or more advanced methods like [[stemming]] and [[lemmatization]]. | |||
===Formation of Bigrams=== | |||
Once the text is tokenized, bigrams can be generated by considering every consecutive pair of words or tokens. For example, given the sentence "The cat sat on the mat," the bigrams generated would be: ("The", "cat"), ("cat", "sat"), ("sat", "on"), ("on", "the"), and ("the", "mat"). | |||
==Applications of Bigrams== | |||
Bigrams are widely used in numerous machine learning and NLP tasks, including but not limited to: | |||
===Language Modeling=== | |||
In [[language modeling]], bigrams are used to estimate the probability of a word occurring, given its preceding word. This helps to capture the local context in a sentence and results in more accurate language models compared to unigram models, which consider words in isolation. | |||
===Text Classification=== | |||
Bigrams can serve as valuable features for [[text classification]] tasks, where the goal is to categorize documents into predefined classes. By incorporating bigram features, classification algorithms can leverage the contextual information present in the text, leading to better performance. | |||
===Information Retrieval=== | |||
In [[information retrieval]], bigrams can improve search engine performance by considering the co-occurrence of words in documents. This helps to identify documents that are more relevant to a given query, as it takes into account the relationship between adjacent words. | |||
===Spell Checking and Correction=== | |||
Bigrams can also be employed in spell checking and correction systems, where they aid in identifying and suggesting corrections for misspelled words based on the surrounding context. | |||
==Explain Like I'm 5 (ELI5)== | |||
A bigram is simply two words that appear together in a sentence or text. Imagine you have a bunch of words written on small pieces of paper, and you want to find out which words often appear side by side. You could look at every pair of neighboring words and start counting how many times they show up together. This is what a bigram does! Bigrams help computers understand the context and meaning of words in a sentence, which can be useful for tasks like predicting what word comes next, figuring out what a piece of writing is about, or finding mistakes in spelling. | |||