Tokenization

Extracted Attributes

• Growth Trend Status

  Identified as a top 3 crypto trend for 2026 by Coinbase CEO Brian Armstrong.

• Regulatory Standard

  PCI Council definitions for Primary Account Number (PAN) replacement.

• Security Components

  Tokenization system, secure vault database, and detokenization interfaces.

• NLP Granularity Types

  Word, subword (BPE, WordPiece), character, phrase, and sentence tokenization.

• Primary Function (NLP)

  Converting text sequences into smaller, discrete units for machine analysis.

• Primary Function (Security)

  Substituting sensitive data with non-sensitive tokens to reduce exposure risk.

Timeline

• The ICLR 2020 Conference highlights significant NLP and NLU papers involving tokenization advancements. (Source: Web Search: Neptune.ai)

  2020-04-26

• DataCamp publishes an updated comprehensive guide on tokenization mechanics and its role in AI upskilling. (Source: Web Search: DataCamp)

  2026-01-15

• At the World Economic Forum in Davos, tokenization is identified as one of the top three growth trends for the crypto industry. (Source: Document 67dd679b-d764-4b4b-b23b-46e6c18ea056)

  2026-01-20

Tokenization (data security)

Tokenization, when applied to data security, is the process of substituting a sensitive data element with a non-sensitive equivalent, referred to as a token, that has no intrinsic or exploitable meaning or value. The token is a reference (i.e. identifier) that maps back to the sensitive data through a tokenization system. The mapping from original data to a token uses methods that render tokens infeasible to reverse in the absence of the tokenization system, for example using tokens created from random numbers. A one-way cryptographic function is used to convert the original data into tokens, making it difficult to recreate the original data without obtaining entry to the tokenization system's resources. To deliver such services, the system maintains a vault database of tokens that are connected to the corresponding sensitive data. Protecting the system vault is vital to the system, and improved processes must be put in place to offer database integrity and physical security. The tokenization system must be secured and validated using security best practices applicable to sensitive data protection, secure storage, audit, authentication and authorization. The tokenization system provides data processing applications with the authority and interfaces to request tokens, or detokenize back to sensitive data. The security and risk reduction benefits of tokenization require that the tokenization system is logically isolated and segmented from data processing systems and applications that previously processed or stored sensitive data replaced by tokens. Only the tokenization system can tokenize data to create tokens, or detokenize back to redeem sensitive data under strict security controls. The token generation method must be proven to have the property that there is no feasible means through direct attack, cryptanalysis, side channel analysis, token mapping table exposure or brute force techniques to reverse tokens back to live data. Replacing live data with tokens in systems is intended to minimize exposure of sensitive data to those applications, stores, people and processes, reducing risk of compromise or accidental exposure and unauthorized access to sensitive data. Applications can operate using tokens instead of live data, with the exception of a small number of trusted applications explicitly permitted to detokenize when strictly necessary for an approved business purpose. Tokenization systems may be operated in-house within a secure isolated segment of the data center, or as a service from a secure service provider. Tokenization may be used to safeguard sensitive data involving, for example, bank accounts, financial statements, medical records, criminal records, driver's licenses, loan applications, stock trades, voter registrations, and other types of personally identifiable information (PII). Tokenization is often used in credit card processing. The PCI Council defines tokenization as "a process by which the primary account number (PAN) is replaced with a surrogate value called a token. A PAN may be linked to a reference number through the tokenization process. In this case, the merchant simply has to retain the token and a reliable third party controls the relationship and holds the PAN. The token may be created independently of the PAN, or the PAN can be used as part of the data input to the tokenization technique. The communication between the merchant and the third-party supplier must be secure to prevent an attacker from intercepting to gain the PAN and the token. De-tokenization is the reverse process of redeeming a token for its associated PAN value. The security of an individual token relies predominantly on the infeasibility of determining the original PAN knowing only the surrogate value". The choice of tokenization as an alternative to other techniques such as encryption will depend on varying regulatory requirements, interpretation, and acceptance by respective auditing or assessment entities. This is in addition to any technical, architectural or operational constraint that tokenization imposes in practical use.

Web Search Results

• The Art of Tokenization: Breaking Down Text for AI - Medium

  Using the trained WordPiece tokenizer: ``` from tokenizers import Tokenizer# Load the tokenizertokenizer = Tokenizer.from_file("wordpiece-tokenizer.json")# Encode a text inputencoded = tokenizer.encode("I have a new GPU!")print("Tokens:", encoded.tokens)print("IDs:", encoded.ids) ``` Output: ``` Tokens: ['I', 'have', 'a', 'new', 'G', '##PU', '!']IDs: [10, 34, 5, 78, 301, 502, 8] ``` ## Conclusion Tokenization is a foundational step in NLP that prepares text data for computational models. By understanding and implementing appropriate tokenization strategies, we enable models to process and generate human language more effectively, setting the stage for advanced topics like word embeddings and language modeling. [...] ### 3. Subword tokenization Splits words into smaller, meaningful subword units. This method balances the granularity of character-level tokenization with the semantic richness of word-level tokenization. Algorithms like Byte-Pair Encoding (BPE) and WordPiece fall under this category. For instance, the BertTokenizer tokenizes `“I have a new GPU!”` as follows: ``` from transformers import BertTokenizertext = "I have a new GPU!"tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")tokens = tokenizer.tokenize(text)print(tokens) ``` Output: ``` ['i', 'have', 'a', 'new', 'gp', '##u', '!'] ``` Here, `“GPU”` is split into `“gp”` and `“##u”`, where `“##”` indicates that `“u”` is a continuation of the previous subword. [...] Standardization: Before tokenizing, the text is standardized to ensure consistency. This may include converting all letters to lowercase, removing punctuation, and applying other normalization techniques. Tokenization: The standardized text is then split into tokens. For example, the sentence `“The quick brown fox jumps over the lazy dog”` can be tokenized into words: ``` ["the", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"] ``` Numerical representation: Since computers operate on numerical data, each token is converted into a numerical representation. This can be as simple as assigning a unique identifier to each token or as complex as creating multi-dimensional vectors that capture the token’s meaning and context.

• Tokenization in NLP: How It Works, Challenges, and Use Cases

  Subword tokenization. Striking a balance between word and character tokenization, this method breaks text into units that might be larger than a single character but smaller than a full word. For instance, "Chatbots" could be tokenized into "Chat" and "bots". This approach is especially useful for languages that form meaning by combining smaller units or when dealing with out-of-vocabulary words in NLP tasks. [...] The primary goal of tokenization is to represent text in a manner that's meaningful for machines without losing its context. By converting text into tokens, algorithms can more easily identify patterns. This pattern recognition is crucial because it makes it possible for machines to understand and respond to human input. For instance, when a machine encounters the word "running", it doesn't see it as a singular entity but rather as a combination of tokens that it can analyze and derive meaning from. To delve deeper into the mechanics, consider the sentence, "Chatbots are helpful." When we tokenize this sentence by words, it transforms into an array of individual words: `["Chatbots", "are", "helpful"].` [...] # Tokenization in NLP: How It Works, Challenges, and Use Cases A guide to NLP preprocessing in machine learning. We cover spaCy, Hugging Face transformers, and how tokenization works in real use cases. Updated Jan 15, 2026 · 10 min read Tokenization, in the realm of Natural Language Processing (NLP) and machine learning, refers to the process of converting a sequence of text into smaller parts, known as tokens. These tokens can be as small as characters or as long as words. The primary reason this process matters is that it helps machines understand human language by breaking it down into bite-sized pieces, which are easier to analyze. ## AI Upskilling for Beginners Learn the fundamentals of AI and ChatGPT from scratch. Learn AI for Free ## What Is Tokenization?

• Tokenization in NLP: Types, Challenges, Examples, Tools

  ## Why do we need tokenization? Tokenization is the first step in any NLP pipeline. It has an important effect on the rest of your pipeline. A tokenizer breaks unstructured data and natural language text into chunks of information that can be considered as discrete elements. The token occurrences in a document can be used directly as a vector representing that document. This immediately turns an unstructured string (text document) into a numerical data structure suitable for machine learning. They can also be used directly by a computer to trigger useful actions and responses. Or they might be used in a machine learning pipeline as features that trigger more complex decisions or behavior. [...] stop word removal, tokenization, stemming. Among these, the most important step is tokenization. It’s the process of breaking a stream of textual data into words, terms, sentences, symbols, or some other meaningful elements called tokens. A lot of open-source tools are available to perform the tokenization process. In this article, we’ll dig further into the importance of tokenization and the different types of it, explore some tools that implement tokenization, and discuss the challenges. ### Read also Best Tools for NLP Projects The Best NLP/NLU Papers from the ICLR 2020 Conference ## Why do we need tokenization? [...] Tokenization can separate sentences, words, characters, or subwords. When we split the text into sentences, we call it sentence tokenization. For words, we call it word tokenization. Example of sentence tokenization Example of word tokenization ## Different tools for tokenization Although tokenization in Python may be simple, we know that it’s the foundation to develop good models and help us understand the text corpus. This section will list a few tools available for tokenizing text content like NLTK, TextBlob, spacy, Gensim, and Keras. ### White Space Tokenization

• Tokenization in NLP: What Is It? | Coursera

  Word tokenization: Word tokenization involves splitting text into individual words. For instance, the sentence “The grass is green” is tokenized into four tokens with this method: [“The”, “grass”, “is”, “green”]. Phrase tokenization: You can also tokenize text into phrases or chunks that convey a specific meaning. For instance, the phrase “Los Angeles” might be a single token instead of two separate words. Sentence tokenization: This type of tokenization segments text into sentences. It separates paragraphs or long blocks of text into distinct sentences for analysis. Subword tokenization: Subword tokenization dissects words into their constituent morphemes, which are the smallest units of meaning in a language. For instance, “unusual” becomes [“un”, “usual”]. [...] Number tokenization: Number tokenization uses digits as the primary component of the token and segments numbers from the rest of the body of text. For example, if the phrase were, “She had 15 cats,” then “15” would be the number token. ## Tokenization NLP examples: Professional uses Many professionals use tokenization for NLP tasks. Regardless of your industry, you can employ tokenization techniques for various information retrieval and analysis tasks. Some common ones include the following: Information retrieval: Tokenization is important in search engines and information retrieval systems. These systems break down information into tokens to better index and analyze information. [...] ### Strengths of tokenization: Enhances data preparation: Tokenization is a fundamental step in preparing text data for NLP tasks to make the text more suitable for machine learning models. Able to control granularity: With different levels of tokenization, you can decide how granular you want your tokens (e.g., characters, subwords, words). Independent of languages: Tokenization techniques can adapt to different languages and scripts to suit different languages. ### Limitations of tokenization: May struggle with ambiguity: Tokenization may struggle with handling language ambiguities. In these cases, you might need a training model and statistical methods to avoid losing tokens.

• What is Tokenization in NLP (Natural Language Processing)? - ixopay

  There are multiple ways that text or speech can be tokenized, although each method’s success relies heavily on the strength of the programming integrated in other parts of the NLP process. Tokenization serves as the first step, taking a complicated data input and transforming it into useful building blocks for the natural language processing program to work with. As natural language processing continues to evolve using deep learning models, humans and machines are able to communicate more efficiently. This is just one of many ways that tokenization is providing a foundation for revolutionary technological leaps. [...] It’s difficult for word tokenization to separate unknown words or Out Of Vocabulary (OOV) words. Word tokenization can also struggle with rare words that are not present in the model's vocabulary, making it less effective for handling infrequent or unseen terms. This is often solved by replacing unknown words with a simple token that communicates that a word is unknown. This is a rough solution, especially since 5 ‘unknown’ word tokens could be 5 completely different unknown words or could all be the exact same word. [...] ## Word Tokenization Word tokenization is the most common version of tokenization. It takes natural breaks, like pauses in speech or spaces in text, and splits the data into its respective words using delimiters (characters like ‘,’ or ‘;’ or ‘“,”‘). While this is the simplest way to separate speech or text into its parts, it does come with some drawbacks.

• Instance Of

  Q4167410