Wondering about what the tokenization is and what its different types are? Read on this article to know about the types of tokenization in detail.
Tokenization has been around for quite some time before the world started to notice it. Tokenization has been utilized for security of credit card information by converting the personal data of customers into strings of characters, which is not vulnerable to hacking. Most recently, the applications of tokenization have been identified in the domain of blockchain and NLP with examples of NFTs.
Therefore, the interest in types of tokenization has increased recently. We have covered the fundamentals of tokenization in another article titled ‘Everything You Need to Know about Tokenization’ you can check right now. The primary focus of the following discussion would revolve around the different tokenization types along with their advantages and drawbacks.
Brief Understanding of Tokenization
Tokenization, in the most basic sense, would imply the conversion of anything into tokens. Even if tokenization found applications in the security of credit card information, it has become an important concept in the domain of NLP. Tokenization is basically essential for breaking down text in natural language processing for enabling improved ease of learning. On the other hand, tokenization in the context of blockchain refers to the conversion of real-world assets into digital assets.
It basically involves mapping information of the real-world objects onto virtual assets. The popularity of non-fungible tokens or NFTs clearly shows the promising roads ahead for tokenization. With that being said, you would be eager to know about “what are the types of tokenization” right now. Let us find out more about the different variants of tokenization you can find commonly in present times.
What Are The Types of Tokenization?
Since tokenization is slowly gaining popularity across various industries, it is important to reflect on the distinct types of tokenization. On the other hand, it is also crucial to find out the variants of tokenization in context of payment processing and NLP use cases. When you are using tokenization for payment processing, then you have the options of vault tokenization and vaultless tokenization.
Similarly, in the case of NLP, you can find different variants of tokenization tailored to distinct requirements such as word tokenization, byte pair encoding (BPE), or sentence tokenization. At the same time, you can also find distinct variants of tokenization in the domain of blockchain applications. Some of them include utility tokens, NFTs, and others.
Here is an in-depth outline of the different tokenization types you can come across –
In traditional payment processing applications, vault tokenization involves the maintenance of a secure database. The secure database is referred to as the tokenization vault database, which stores sensitive data. At the same time, the tokenization vault database also stores the corresponding non-sensitive data for the sensitive information. Users could easily decrypt the newly tokenized data with the help of sensitive and non-sensitive data tables. The most prominent setback in vault tokenization is the extended processing time for detokenization due to expansion in size of the database.
Another prominent answer to ‘what are the types of tokenization’ in traditional payment processing use cases refers to vaultless tokenization. It is a highly efficient and safer alternative than vault tokenization. Rather than maintaining a database, vaultless tokenization focuses on using secure cryptographic devices. The secure cryptographic devices leverage algorithms based on certain standards for conversion of sensitive data to non-sensitive data. The tokens created in vaultless tokenization could be decrypted for obtaining original data without a tokenization vault database.
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Tokenization Types in NLP
Tokenization is one of the basic tasks in the domain of natural language processing or NLP. It involves the separation of a piece of text into smaller units referred to as tokens for enabling machines to understand natural text. You can divide a piece of text into words, characters, or just subwords, according to your requirements. Therefore, the types of tokenization in NLP are broadly classified into three categories. Let us learn more about the tokenization variants in the case of NLP.
Word tokenization is one of the most commonly used tokenization types in natural language processing. It involves splitting a particular piece of text into individual words according to a specific delimiter. The delimiter helps in determining the formation of various word-level tokens.
The examples of pre-trained word embedding come under the scope of word tokenization. However, word tokenization could encounter a formidable setback in the form of out of vocabulary or OOV words. The OOV words basically point out the new words you can find at the time of testing. Another prominent setback in word tokenization refers to the size of the vocabulary.
The problem of a large vocabulary and possible chances of coming across new words create the foundation for character tokenization. Character tokenization is one of the notable types of tokenization applied in the case of NLP. It involves splitting particular text data into the collection of characters. Interestingly, character tokenization could help in addressing various notable setbacks evident with word tokenization.
Character tokenization could help in effective management of OOV words by safeguarding information about the concerned word. It helps in breaking down the out-of-vocabulary word into characters, followed by a representation of the word in terms of characters. Furthermore, character tokenization also works effectively in restricting the size of your vocabulary.
Even if character tokenization is a trustworthy mention among tokenization types for NLP, it has some drawbacks. One of the prominent issues in character tokenization refers to the rapid growth in length of input and output sentences. Therefore, it could be pretty challenging to discover the relationship between the characters for rounding up meaningful words.
Also Check: An Overview of Tokenization Algorithms in NLP
The setbacks in character tokenization provide the foundation for another notable entry among types of tokenization in natural language processing. Subword tokenization, as the name implies, helps in dividing a given text into different subwords. So, what are subwords? The words such as lower can be divided as low-er, and simplest could be divided into simple-st. The transformation-based NLP models depend on subword tokenization for preparing their vocabulary. One of the most common methods used for subword tokenization refers to Byte Pair Encoding or BPE.
Byte Pair Encoding or BPE is a popular tokenization method applicable in the case of transformer-based NLP models. BPE helps in resolving the prominent concerns associated with word and character tokenization. Subword tokenization with BPE helps in effectively tackling the concerns of out-of-vocabulary words.
BPE can help in segmentation of OOV words as subwords followed by representing the word with respect to the subwords. The input and output sentence lengths after BPE are shorter in comparison to those in character tokenization. BPE is basically a word segmentation algorithm that helps in merging the characters or character sequences frequently occurring in a repetitive fashion.
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Tokenization Types in Blockchain
When you are looking at the different types of tokenization in blockchain, you will come across digital assets that are suitable for trading in the ecosystem of a blockchain project. The different variants of tokenization with respect to their applications in blockchain include platform tokens, governance tokens, utility tokens, and non-fungible tokens or NFTs.
Platform tokenization basically refers to issuing tokens to blockchain infrastructures for developing decentralized applications. One of the commonly noted examples of platform tokenization refers to DAI, which can help in facilitating smart contract transactions. Platform tokenization draws benefits from the blockchain network used as the foundation for improved security and support for transactional activity.
Utility tokenization is the process of creating utility tokens in a specific protocol for accessing the services in the concerned protocol. It is important to note that utility tokenization does not involve creating tokens for direct investment. Utility tokens offer necessary platform activity for strengthening the platform’s economy while the platform offers security to the tokens.
The growth of decentralized protocols has called for another notable alternative among tokenization types for blockchain. Governance tokenization focuses on blockchain-based voting systems as they could refine the decision-making process around decentralized protocols. The benefit of governance tokenization is evident in the value of on-chain governance for enabling all stakeholders with abilities for collaboration, debating, and voting on the management of a system.
The final and one of the most popular entries among the types of tokenization in blockchain refer to NFTs. Non-fungible tokens provide a digital representation of unique assets, and this type of tokenization has prolific use cases. For example, digital artists could get better opportunities for managing ownership and trading of their work. The world has recently witnessed a massive surge in demand for NFTs and NFT-based application development. Therefore, it is reasonable to focus on the creation of NFTs as a prominent variant of tokenization.
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On a final note, it is quite clear that tokenization has wide-ranging classifications depending on the context. In the case of traditional payment processing applications, tokenization included the categories of vault tokenization and vaultless tokenization. When you take a look at ‘what are the types of tokenization in NLP,’ you would find word tokenization, character tokenization, and subword tokenization.
On the other hand, the tokenization variants in blockchain applications included platform tokenization, utility tokenization, governance tokenization, and NFTs. You can learn more about tokenization in detail and explore the challenges and limitations for its long terms growth. Find the ideal sources of information and training resources on tokenization right now!
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Author: Diego Geroni