unigram Sentences

The probability distribution of unigrams is often used in language models to predict the next word in a sentence.

In text analysis, unigrams are the building blocks for more complex n-gram models, such as bigrams and trigrams.

Unigrams in a bag-of-words model can be employed for text classification tasks.

Spelling correction algorithms can use unigram models to determine the most probable correct version of a misspelled word.

Unigrams are a fundamental unit in the statistical analysis of word frequencies in a corpus.

The frequency count of unigrams can help in identifying common words in a text.

Unigrams are used in the generation of n-gram models for language modeling purposes.

In text processing, unigrams can be used to create histograms of word frequencies.

Unigrams are often used as a baseline in machine learning tasks for natural language processing.

Unigrams provide the simplest form of word-based frequency distribution in a text.

Unigrams are the first step in understanding the basic structure of a text corpus.

In computational linguistics, unigrams are essential for simple probability calculations.

Unigrams can be used in spell-checking software to suggest corrections based on the frequency of occurrence of words.

The analysis of unigrams is crucial for understanding the composition of a text's vocabulary.

Unigrams are used in the simplest form of language modeling to predict the next word in a sentence.

Unigrams are often used in the calculation of word frequency for documents in information retrieval systems.

Using unigrams, we can generate a frequency distribution table for a given text corpus.

Unigrams serve as a starting point in more advanced textual analysis and natural language processing techniques.

Unigrams provide a basic framework for statistical language modeling.