📌 Tokenisation Strategies Summary
Tokenisation strategies are methods used to split text into smaller pieces called tokens, which can be words, characters, or subwords. These strategies help computers process and understand language by breaking it down into more manageable parts. The choice of strategy can affect how well a computer model understands and generates text, as different languages and tasks may require different approaches.
🙋🏻♂️ Explain Tokenisation Strategies Simply
Imagine cutting a loaf of bread into slices so it is easier to eat. Tokenisation is like slicing up sentences so a computer can understand each piece. Depending on the recipe, you might cut the bread into thick or thin slices, just like different strategies cut text into bigger or smaller parts.
📅 How Can it be used?
A chatbot project might use tokenisation strategies to break user messages into words or subwords for better understanding and response.
🗺️ Real World Examples
In machine translation, tokenisation strategies are used to split sentences into words or subword units so that a translation model can accurately translate each part and handle unfamiliar or compound words.
A search engine uses tokenisation to break down search queries into separate words, making it easier to match user input with relevant documents and improve search accuracy.
✅ FAQ
Why is it important to break text into smaller pieces using tokenisation strategies?
Breaking text into smaller pieces helps computers make sense of language. By splitting text into words, characters, or even parts of words, computers can more easily analyse and process information. This makes it possible for apps like translators and chatbots to understand and respond to what we write.
Do tokenisation strategies work the same for all languages?
No, different languages can need different tokenisation strategies. For example, English uses spaces to separate words, but some Asian languages do not use spaces in the same way. This means the strategy used for one language might not work as well for another, so it is important to choose the right method for the language at hand.
Can the choice of tokenisation strategy affect how well a computer understands text?
Yes, the way text is split into tokens can have a big impact on how accurately a computer can understand and generate language. The right strategy helps models pick up on meaning and context, while a poor choice might lead to confusion or misunderstandings in the final result.
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