What is Token Classification Concept and definition. Glossary (2024)

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What is Token Classification Concept and definition. Glossary? ›

Token classification refers to the process of assigning a label or category to each token or element in a text stream. Tokens can be individual words, numbers, symbols and other elements in a text.

The token concept is a distributed solution that guarantees that during a critical section, only one program has access to an object in the memory, a database table, or a system resource, for example. The idea is simple: For the object (or objects) that can be accessed exclusively only, there is a token.

Token classification refers to the classifications of tokens in a squence. So for example you assign classes to words in a sentece. In sequence classification you're classifying the whole sequence, for example assigning a class to a sentence.

Token classification assigns a label to individual tokens in a sentence. One of the most common token classification tasks is Named Entity Recognition (NER). NER attempts to find a label for each entity in a sentence, such as a person, location, or organization.

Token classification is a natural language understanding task in which a label is predicted for each token in a piece of text. This is different from text classification because each token within the text receives a prediction.

In general, a token is an object that represents something else, such as another object (either physical or virtual), or an abstract concept as, for example, a gift is sometimes referred to as a token of the giver's esteem for the recipient.

Tokenization refers to a process by which a piece of sensitive data, such as a credit card number, is replaced by a surrogate value known as a token. The sensitive data still generally needs to be stored securely at one centralized location for subsequent reference and requires strong protections around it.

Token classification is a machine learning model that helps computers understand the meaning and type of words in sentences. In this article, we'll explore token classification in simple terms and discuss its various applications.

Token classification involves labelling each token with a specific category based on its meaning or function in the text. For example, in a sentence, verbs can be labelled as "VERB", nouns as "NOUN", adjectives as "ADJECTIVE", and so on.

Sequence Classification (or Text Classification) is the NLP task of predicting a label for a sequence of words. For example, a string of That movie was terrible because the acting was bad could be tagged with a label of negative .

What is the difference between NER and POS? ›

With POS tagging, we can identify the different parts of speech in a sentence, which can be useful for tasks like sentiment analysis and text classification. Meanwhile, NER helps us to identify and classify named entities in text, such as people, organizations, locations, and dates.

The type–token distinction is the difference between a class (type) of objects and the individual instances (tokens) of that class.

Low-Rank Adaptation (LoRA) is a reparametrization method that aims to reduce the number of trainable parameters with low-rank representations. The weight matrix is broken down into low-rank matrices that are trained and updated. All the pretrained model parameters remain frozen.

We can think of a token as a small unit that can easily be understood by a large language model. What do I mean by that and how is that different than a word? Well, if you think about the word everyday, you can sort of break it into two tokens: every and day. Now, breaking this down helps the model process this input.

English: 1 word ≈ 1.3 tokens. French: 1 word ≈ 2 tokens.

Text classification techniques are divided to three categories: rule based, statistics based and machine learning based. Each of these categories can either be implemented on the syntactic level, the morphological level, the semantic level or the lexical level of the text being analyzed.

Process tokens are an abstract concept in BPMN. They refer to the current point of execution within a process. A business process can have multiple tokens that indicate that the process is running in multiple paths. For example, gateways are often used to split the path of a process.

Token-based approach definition

Token-based approaches use something you own to make a personal identification, such as a passport, driver's license, ID card, credit card, keys or badges, which can be lost or stolen.

Tokens can represent assets, including physical assets like real estate or art, financial assets like equities or bonds, intangible assets like intellectual property, or even identity and data.

The theory which is usually called the token identity theory by philosophers is normally stated and argued for as a theory about mental events; it is often said, for example, to be the view that all mental events are physical events.