Trigram

Revision as of 19:04, 18 March 2023 by Walle (talk | contribs) (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...")
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
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 Estimation

Trigrams are often employed in language modeling to estimate the probability of a word given its preceding words. In a trigram model, the probability of a word occurring depends on the previous two words. This is expressed mathematically as:

P(word_n | word_n-1, word_n-2)

By utilizing trigram probabilities, language models can generate more accurate predictions, as they capture not only the immediate context (bigram) but also a broader context of the surrounding words.

Smoothing Techniques

Trigram language models can suffer from data sparsity, as not all possible combinations of three words may be present in the training data. To overcome this issue, smoothing techniques are applied to assign non-zero probabilities to unseen trigrams. Common smoothing methods include additive smoothing (also known as Laplace smoothing), Good-Turing discounting, and Kneser-Ney smoothing.

Feature Extraction

Trigrams can be employed as features for various machine learning tasks, such as text classification, sentiment analysis, and information retrieval. In these applications, the presence or frequency of trigrams in a text document can serve as a discriminative feature, helping algorithms to identify patterns and make predictions.

For example, in sentiment analysis, trigrams can help capture subtle nuances in the sentiment expressed in a text, as they provide more context than unigrams (individual words) or bigrams (pairs of consecutive words).

Explain Like I'm 5 (ELI5)

Imagine you have a bag of letter blocks, and you want to arrange them to make sentences. A trigram is like picking up three blocks in a row and seeing how they fit together. In the world of computers and language, trigrams help the computer understand how words are usually put together, so it can make better guesses about what comes next. This helps the computer make sense of what people are saying and even make its own sentences that sound more natural.