What is Tokenization?

Tokenization: The First Step to Making Sense of Text in Natural Language Processing

In order for natural language processing (NLP) and artificial intelligence models to make sense of texts, they need to be broken down into smaller units. This process is called tokenization. Tokenization breaks text into smaller pieces, allowing machine learning and AI systems to process these pieces more easily. In this article, we will explore what tokenization is, how it works and why it is so important in natural language processing models.

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What is Tokenization?

Tokenization is the process of breaking a text into smaller meaningful units (tokens) such as words, sentences or characters. Each token is a meaningful and processable unit for language models. Tokenization is a key step in AI and language models because it makes the raw text machine-understandable.

For example, in the sentence “Artificial intelligence is changing the world”, tokenization can be done as follows:

• Word-based tokenization: [“Artificial”, “intelligence”, “is”, “changing”, “the”, “world”]
• Character-based tokenization: [“A”, “r”, “t”, “i”, “f”, “i”, “c”, “i”, “a”, “l”, “ ”, “i”, “n”, “t”, “e”, “l”, “l”, “i”, “g”, “e”, “n”, “c”, “e”, “,”, “i”, “s”, “ ”, “c”, “h”, “a”, “n”, “g”, “i”, “n”, “g”, “ ”,  “t”, “h”, “e”, “ ”, “w”, “o”, “r”, “l”, “d”]

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The goal of tokenization is to break down text into smaller and more processable units in order to understand the language structure and meaning relations within the text. This plays a critical role in the training process of language models.

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How Does Tokenization Work?

Tokenization is the first step in text analysis, and this process usually involves the following steps:

1. Text Preparation: Tokenization starts by first cleaning the text. At this stage, unnecessary symbols, spaces or punctuation marks can be removed or kept.
2. Identification of Tokens: Text is broken down into specific units. Tokens can usually be words or characters. In some cases, subword tokenization is also used. Especially in complex languages, subword tokenization allows for more accurate results by breaking words into smaller pieces.
3. Preprocessing: Additional preprocessing can be performed on the resulting tokens. For example, uppercase letters can be converted to lowercase letters, stopwords (meaningless words) can be removed, or words can be reduced to their roots (stemming or lemmatization).
4. Tokenization is a critical step for training and processing language models. A proper tokenization process helps the model to better understand the language structure and helps the results to be more successful.

Tokenization is a critical step for training and processing language models. A proper tokenization process helps the model to better understand the language structure and helps the results to be more successful.

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Types of Tokenization

Tokenization, uygulama alanına ve dil yapısına göre farklı şekillerde gerçekleştirilebilir. İşte en yaygın tokenization yöntemleri:Tokenization can be performed in different ways depending on the application area and language structure. Here are the most common tokenization methods:

1. Word-based Tokenization: In this method, text is divided into words. It is the most common and simple method. However, this method may be insufficient in some languages or for very long words.
2. Character Based Tokenization: The text is broken down into individual characters. It is used especially in some NLP projects to learn the finer structure of the language. However, this method generates a lot of tokens, which can increase the transaction cost.
3. Subword Tokenization: The text is divided into subword units. This method is especially preferred for rare words or when the morphological structure of the language is complex. Techniques such as BPE (Byte Pair Encoding) are commonly used for subword tokenization.

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Tokenization and Natural Language Processing

Tokenization plays a crucial role in natural language processing models. In order for AI systems to make sense of language, texts need to be broken down into small pieces. For example, models such as GPT (Generative Pre-trained Transformer) make sense of text with tokenization and generate text by processing these tokens. The correct implementation of tokenization directly affects the performance of the model.

For example, if a language model can learn tokens in a meaningful way during the training process, it will be more successful in text generation or comprehension tasks using this information in the next steps. This is why tokenization is a critical component of every NLP project.

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Challenges of Tokenization

Tokenization is not always a simple process and can involve some challenges:

1. Language Differences: Since different languages have different grammar and word structure, the same tokenization strategy cannot be used for every language. For example, in character-based languages such as Chinese, word-based tokenization may not be sufficient.
2. Multi-word Expressions: Some multi-word phrases contain a single meaning and should therefore be considered as a single token. For example, phrases such as “machine learning” are composed of two words but express a single concept.
3. Abbreviations and Symbols: Abbreviations, numbers and symbols can be handled in different ways during tokenization. Such special characters need to be handled correctly.

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Tokenization and Advanced Models

Advanced language models use various techniques to make the tokenization process more efficient. For example, large language models (LLMs) learn from larger and more meaningful data sets by subdividing the text into chunks with subword tokenization. This method allows for a better understanding of the morphological structure of the language and accurate processing of even rare words.

Likewise, transformer-based models produce more effective results by processing data with tokenization. Combined with the attention mechanism and other techniques, the tokenization process plays a major role in the success of language models.

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The Future of Tokenization

As artificial intelligence and natural language processing models develop, the tokenization process is expected to become more advanced. Especially in projects where the morphological structure of languages is complex, techniques such as subword tokenization will be used more and more. At the same time, it will be possible to further optimize the tokenization process with advanced learning methods such as self-supervised learning and reinforcement learning.

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Conclusion: Successful NLP Models with Proper Tokenization

Tokenization is a fundamental step in natural language processing projects and allows models to make sense of language by splitting text into smaller units. The right tokenization process directly affects the performance of the model and it is of great importance to choose strategies that are suitable for the structure of the language.