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Trigram: Difference between revisions
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(Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning and natural language processing (NLP), a '''trigram''' is a continuous sequence of three items from a given sample of text or speech. Trigrams are a type of n-gram, where ''n'' represents the number of items in the sequence. N-grams are used in various language modeling and feature extraction tasks to analyze and predict text data. ==Language Modeling== ===Probability Estimatio...") |
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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, trigrams are a type of n-gram model, specifically a sequence of three consecutive items, usually words or characters, from a given text. Trigrams are used in various natural language processing (NLP) tasks, such as language modeling, text classification, and information retrieval, to capture the contextual information within the text. This article will provide a detailed overview of trigrams, their applications, and methods of implementation in machine learning. | |||
==N-grams in Natural Language Processing== | |||
N-grams are a fundamental concept in natural language processing, used to model and represent the relationships between items in a sequence. The general term 'n-gram' refers to a contiguous sequence of n items, where n can be any integer value. | |||
===Unigrams=== | |||
Unigrams are the simplest form of n-grams, representing individual items within a sequence. In the context of text processing, a unigram would be a single word or character. | |||
===Bigrams=== | |||
Bigrams are sequences of two consecutive items. In text processing, a bigram would consist of two consecutive words or characters, capturing the relationship between the two items. | |||
===Trigrams=== | |||
Trigrams, the focus of this article, are sequences of three consecutive items. In text processing, a trigram would consist of three consecutive words or characters. Trigrams are useful for modeling the context and relationships among three items, which can improve the performance of machine learning algorithms in various NLP tasks. | |||
==Applications of Trigrams== | |||
Trigrams are used in a variety of NLP tasks to capture contextual information and improve the performance of machine learning algorithms. Some common applications include: | |||
===Language Modeling=== | |||
Trigrams are often used in language modeling to estimate the probability of a word or character occurring in a sequence, given the previous two words or characters. This allows for more accurate predictions by taking into account the context provided by the trigram. | |||
===Text Classification=== | |||
In text classification tasks, such as sentiment analysis or topic classification, trigrams can be used to extract features from the text data, which can then be used by machine learning algorithms to classify the text into various categories. | |||
===Information Retrieval=== | |||
Trigrams can be employed in information retrieval systems, such as search engines, to improve the relevance of search results. By considering trigram occurrences in the text, search engines can better understand the context and relationships among the query terms, leading to more accurate and relevant search results. | |||
==Explain Like I'm 5 (ELI5)== | |||
Imagine you're trying to understand a sentence or a story. You can think of trigrams as groups of three words or characters that help you understand what's happening. By looking at these groups of three, you can better understand the relationships between the words and how they work together. Trigrams are used in computer programs to help them understand and process language, like when you search for something on the internet or when a computer tries to figure out if a sentence is happy or sad. | |||