๐—จ๐—ป๐—ฑ๐—ฒ๐—ฟ๐˜€๐˜๐—ฎ๐—ป๐—ฑ๐—ถ๐—ป๐—ด ๐—ง๐—ผ๐—ธ๐—ฒ๐—ป๐—ถ๐˜‡๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ถ๐—ป ๐—ก๐—Ÿ๐—ฃ: ๐—•๐—ฒ๐—ป๐—ฒ๐—ณ๐—ถ๐˜๐˜€ ๐—ฎ๐—ป๐—ฑ ๐——๐—ฟ๐—ฎ๐˜„๐—ฏ๐—ฎ๐—ฐ๐—ธ๐˜€

Tokenization is a fundamental process in Natural Language Processing (NLP) that involves breaking down text into smaller units called tokens. These tokens can be words, subwords, or characters, enabling models to process and understand text effectively. Each tokenization method has its own set of advantages and disadvantages.

๐‘ป๐’š๐’‘๐’†๐’” ๐’๐’‡ ๐‘ป๐’๐’Œ๐’†๐’๐’Š๐’›๐’‚๐’•๐’Š๐’๐’

  1. ๐‘พ๐’๐’“๐’…-๐‘ฉ๐’‚๐’”๐’†๐’… ๐‘ป๐’๐’Œ๐’†๐’๐’Š๐’›๐’‚๐’•๐’Š๐’๐’

Description: Splits text into individual words based on spaces and punctuation.

:arrow_right:Advantages:

Simple and intuitive for languages with clear word boundaries.

Maintains the semantic integrity of words.

:arrow_right:Disadvantages:

Struggles with out-of-vocabulary (OOV) words, leading to the need for an extensive vocabulary.

Inefficient for languages with complex morphology or where words are concatenated.

  1. ๐‘ช๐’‰๐’‚๐’“๐’‚๐’„๐’•๐’†๐’“-๐‘ฉ๐’‚๐’”๐’†๐’… ๐‘ป๐’๐’Œ๐’†๐’๐’Š๐’›๐’‚๐’•๐’Š๐’๐’

Description: Divides text into individual characters.

:arrow_right:Advantages:

Eliminates OOV issues, as every word is decomposed into characters.

Simplifies the vocabulary to a manageable size.

:arrow_right:Disadvantages:

Produces longer sequences, increasing computational complexity.

May lose meaningful word-level information, making it harder for models to learn context.

  1. ๐‘บ๐’–๐’ƒ๐’˜๐’๐’“๐’… ๐‘ป๐’๐’Œ๐’†๐’๐’Š๐’›๐’‚๐’•๐’Š๐’๐’

Description: Breaks words into subword units, such as prefixes, suffixes, or roots.

:arrow_right:Advantages:

Balances vocabulary size and the ability to handle OOV words.

Captures meaningful subword patterns, aiding in understanding and generation.

:arrow_right:Disadvantages:

May introduce ambiguities in tasks requiring precise character-level analysis.

Reconstructing the original text from subwords can be challenging.

  1. Byte-Pair Encoding (BPE)

Description: Originally a data compression algorithm, BPE iteratively merges the most frequent pairs of characters or character sequences in a corpus to form new tokens.

:arrow_right:Advantages:

Effectively handles rare and OOV words by decomposing them into known subword units.

Allows for a flexible vocabulary size based on the desired number of merges.

:arrow_right:Disadvantages:

The merging process can result in tokens that do not align with linguistic boundaries, potentially affecting interpretability.

Requires careful determination of the number of merges to balance vocabulary size and model performance.

๐‘ฌ๐’™๐’‚๐’Ž๐’‘๐’๐’† ๐‘ฏ๐’Š๐’ˆ๐’‰๐’๐’Š๐’ˆ๐’‰๐’•๐’Š๐’๐’ˆ ๐‘บ๐’–๐’ƒ๐’˜๐’๐’“๐’… ๐‘ป๐’๐’Œ๐’†๐’๐’Š๐’›๐’‚๐’•๐’Š๐’๐’ ๐‘ช๐’‰๐’‚๐’๐’๐’†๐’๐’ˆ๐’†๐’”

Consider the French question: โ€œCombien de โ€˜Aโ€™ y a-t-il dans le mot โ€˜Anatomieโ€™ ?โ€ (How many 'Aโ€™s are there in the word โ€˜Anatomieโ€™?). A subword tokenizer might split โ€˜Anatomieโ€™ into [โ€˜Aโ€™, โ€˜nโ€™, โ€˜atoโ€™, โ€˜mieโ€™]. Counting the 'Aโ€™s in these subwords could lead the model to incorrectly conclude there are three 'Aโ€™s, whereas the correct answer is two. This example illustrates how subword tokenization can sometimes misrepresent character-level details.

๐‚๐จ๐ง๐œ๐ฅ๐ฎ๐ฌ๐ข๐จ๐ง

Choosing the appropriate tokenization method is crucial in NLP, as it directly impacts a modelโ€™s performance and accuracy. Understanding the strengths and limitations of each approach allows practitioners to select the most suitable method for their specific application.

did i miss a tokenization method?

Iโ€™m a bit curious why you posted this. Itโ€™s information surely, but not really part of a discussion or question.

good question !
only that I was looking for more of these NLP topics by digging deeper, so I thought Iโ€™d share them if it can help someone.

in addition it is not placed in the course section.

Thanks!

I guess models can do better tokenization than hardcoded algorithms.