Natural Language Processing: Tokenization (Basic) | Summary and Q&A

TL;DR

Tokenization is the process of dividing input sentences into smaller chunks known as tokens, which can be done in various ways using techniques like splitting by spaces or using regular expressions.

Key Insights

• 🛩️ Tokenization is the process of dividing input sentences into smaller chunks or subwords known as tokens.
• 😑 Different tokenization techniques exist, such as splitting by spaces or using regular expressions to replace symbols.
• 🎰 Tokenization helps in reducing the amount of training data required for machine learning models in NLP tasks.
• 😑 Regular expressions can be used to create custom tokenization functions for specific requirements.
• ❓ Tokenization is an essential step in various NLP tasks, including sentiment analysis, entity extraction, and language modeling.
• ❓ Advanced tokenization techniques like byte pair encoding and wordpiece tokenization provide more efficient handling of complex languages.
• 📚 The NLTK library offers useful functions for tokenization, such as word_tokenize and sent_tokenize.

Transcript

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Questions & Answers

Q: What is tokenization?

Tokenization is the process of dividing input sentences into smaller chunks or subwords known as tokens. It helps in reducing the amount of training data required for machine learning models.

Q: How can tokenization be done using spaces?

Tokenization using spaces involves splitting the input sentence by spaces, resulting in individual words or subwords becoming tokens. Punctuation marks and symbols can also be considered as separate tokens.

Q: What is the role of regular expressions in tokenization?

Regular expressions can be used to replace symbols with spaces, allowing for more advanced tokenization techniques. They can help in identifying and splitting sentences based on specific patterns or characters.

Q: How does tokenization help in natural language processing?

Tokenization helps in reducing the complexity of language data by converting sentences into smaller units. It enables better analysis and processing of text for tasks like sentiment analysis, entity extraction, and language modeling.

Summary & Key Takeaways

• Tokenization is the process of dividing input sentences into smaller chunks or subwords known as tokens.

• Tokens can be created by splitting the sentence by spaces or using regular expressions to replace symbols with spaces.

• Tokenization helps reduce the amount of training data required for machine learning models and is essential in NLP.