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N-gram and Skip-gram Extractor

By Koray Tuğberk GÜBÜR Chats：1 Added time: 2024-03-28 Gpt updated time: 2024-02-19
Turn Amazon Comment Pages into real-world customer reviews with details.
Use N-gram and Skip-gram Extractor on ChatGPT
Text Analysis Natural Language Processing

Functions of N-gram and Skip-gram Extractor on ChatGPT

Who is suitable to use N-gram and Skip-gram Extractor on ChatGPT?

N-gram and Skip-gram Extractor on ChatGPT

How do I use N-gram and Skip-gram Extractor Quickstart on ChatGPT?

1. Install the ChatGPT N-gram and Skip-gram Extractor library. 2. Import the library and instantiate the extractor object. 3. Provide the text or set of texts to analyze. 4. Specify the n-gram or skip-gram size. 5. Retrieve the extracted n-grams or skip-grams.

How to use N-gram and Skip-gram Extractor on ChatGPT?

To use the N-gram and Skip-gram Extractor on ChatGPT, simply input a text or a set of texts and specify the desired n-gram or skip-gram size. The extractor will then analyze the input and generate the corresponding n-grams or skip-grams.

N-gram and Skip-gram Extractor on ChatGPT's Tags

Text Analysis Language Processing N-gram Skip-gram

FAQ about N-gram and Skip-gram Extractor on ChatGPT

What is an n-gram?
An n-gram is a contiguous sequence of n items from a given sample of text or speech. For example, in the sentence 'I love natural language processing,' the 2-grams (or bigrams) are 'I love,' 'love natural,' 'natural language,' and 'language processing.'
What is a skip-gram?
A skip-gram is a variation of an n-gram that allows for skipping one or more items in the sequence. It is useful for capturing context and relationships between non-adjacent words. For example, in the sentence 'I love natural language processing,' a skip-2-gram would include pairs such as 'I natural,' 'love language,' and 'natural processing.'
Is the ChatGPT N-gram and Skip-gram Extractor available for multiple programming languages?
Yes, the ChatGPT N-gram and Skip-gram Extractor library is available for multiple programming languages including Python, JavaScript, and Java. It provides a convenient way to extract n-grams and skip-grams from text data.

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