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Keyword N-Gram Viewer & Analysis Tool

In natural language processing, an "n-gram" is a contiguous sequence of n items, typically words or characters, extracted from a given sample of text. The "n" in n-gram represents the number of items in the sequence. For example, a bigram (2-gram) involves pairs of consecutive words, while a trigram (3-gram) consists of triplets of words.

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An N-gram tool is a computational linguistic tool used in natural language processing (NLP) and text analysis to analyze and understand the structure and patterns within a given set of text. N-grams are contiguous sequences of N items (words, characters, or other units) from a given sample of text or speech.

Here's a breakdown of the key components and concepts related to N-gram tools:

1. N-gram:

An N-gram is a sequence of N items, typically words, characters, or tokens. Examples: Unigram (1-gram): "apple," "banana," "cherry" Bigram (2-gram): "apple banana," "banana cherry" Trigram (3-gram): "apple banana cherry"

2. Tokenization:

The process of breaking down a text into individual units, such as words or characters. This step is crucial for N-gram analysis, as it defines the items used to form the N-grams.

3.Frequency Analysis:

N-gram tools are often used for frequency analysis to identify the most common sequences of words or characters in a given text.

4.Language Modeling:

N-gram models are used in language modeling to predict the probability of a word or sequence of words based on the context provided by the preceding N-1 words.

5.Text Prediction:

N-gram models can be applied to predict the next word or sequence of words in a given context. This is commonly used in applications like autocomplete or text suggestion.

6.Statistical Language Processing:

N-gram models are part of statistical language processing techniques, providing insights into the structure and patterns of natural language.

7.N-gram Order:

The "N" in N-gram represents the order of the model, indicating the number of items in each sequence. Higher N values capture longer-range dependencies but may require more data and computational resources.


Techniques like Laplace smoothing or other smoothing methods are often applied to handle unseen N-grams and improve the robustness of the model.

N-gram tools are widely used in various NLP tasks, including machine translation, speech recognition, information retrieval, and text classification. They offer a simple yet effective way to analyze and model the sequential structure of natural language.