"Tokenizers" Example Sentences
1. "Tokenizers" are used to break a stream of text into smaller parts called tokens.
2. A "tokenizer" can be used to break a sentence into its component words.
3. "Tokenizers" are essential in natural language processing tasks.
4. Natural language processing applications often require "tokenizers" to process text.
5. "Tokenizers" are used to split text into tokens, which can then be used for further processing.
6. "Tokenizers" are used to identify words, phrases, and other units of text.
7. "Tokenizers" can be used to break a text into its component words, phrases, and other units.
8. Some "tokenizers" are designed to recognize certain types of text, such as dates and numbers.
9. "Tokenizers" are used to break a text into its component words, phrases, and other units for further processing.
10. "Tokenizers" are commonly used in natural language processing applications to process text.
11. "Tokenizers" can be used to split text into tokens which can then be used for further processing.
12. "Tokenizers" are used in natural language processing to break a text into its component words and phrases.
13. "Tokenizers" are used to identify words, phrases, and other units of text in a stream of text.
14. Natural language processing applications often require the use of "tokenizers" to process text.
15. "Tokenizers" are used to break a stream of text into smaller parts called tokens which can then be used for further processing.
16. "Tokenizers" are used to break a stream of text into smaller parts called tokens for further processing.
17. "Tokenizers" are essential for natural language processing tasks, as they are used to break a text into its component words and phrases.
18. "Tokenizers" are used to identify words, phrases, and other units of text in a stream of text for further processing.
19. Natural language processing applications require "tokenizers" to break a text into its component words, phrases, and other units.
20. "Tokenizers" are used to break a text into its component words, phrases, and other units for further natural language processing tasks.
Common Phases
1. Identify the text;
2. Split the text into tokens;
3. Remove stop words;
4. Stem the tokens;
5. Tag the tokens;
6. Create n-grams;
7. Perform sentiment analysis.