Annotator: meaning, definitions and examples
๐๏ธ
annotator
[ หรฆnษหtoสtษr ]
person role
An annotator is a person or tool that examines and adds annotations, notes, or explanations to a text, image, or data set. Annotators play a crucial role in fields like data science, machine learning, and linguistic research by providing meaningful insights that enhance understanding and processing of information.
Synonyms
Examples of usage
- The annotator highlighted key themes in the manuscript.
- She worked as an annotator for the research project.
- The software includes an annotator feature for image tagging.
machine learning
In machine learning, an annotator refers to software or a system that labels data sets with relevant tags or categories. This process is essential for training algorithms and improving their accuracy in recognizing patterns and making predictions.
Synonyms
Examples of usage
- The dataset was processed by an annotator to prepare it for analysis.
- A good annotator can significantly reduce the time spent on data preparation.
- Automated annotators are becoming increasingly common in AI research.
Translations
Translations of the word "annotator" in other languages:
๐ต๐น anotador
๐ฎ๐ณ เคเคจเฅเคเฅเคเคฐ
๐ฉ๐ช Annotator
๐ฎ๐ฉ anotator
๐บ๐ฆ ะฐะฝะพัะฐัะพั
๐ต๐ฑ anotator
๐ฏ๐ต ใขใใใผใฟใผ
๐ซ๐ท annotateur
๐ช๐ธ anotador
๐น๐ท notatรถr
๐ฐ๐ท ์ฃผ์์
๐ธ๐ฆ ู ูุนููููู
๐จ๐ฟ anotรกtor
๐ธ๐ฐ anotรกtor
๐จ๐ณ ๆณจ้่
๐ธ๐ฎ anotator
๐ฎ๐ธ athugasendari
๐ฐ๐ฟ ะฐะฝะฝะพัะฐัะพัะผะตะฝ
๐ฌ๐ช แแแแขแแขแแ แ
๐ฆ๐ฟ annotator
๐ฒ๐ฝ anotador
Etymology
The word 'annotator' originates from the Latin 'annotare', meaning 'to note or to add notes'. This Latin term combines 'ad-', meaning 'to', and 'notare', meaning 'to mark or to note'. The usage of the term in the context of adding comments or notes to texts evolved over the centuries, particularly with the rise of scholarly and literary works that required detailed explanations or references. By the 20th century, the term became more prevalent in academic and technological fields, particularly in the context of computer science and data processing. As the importance of data labeling in machine learning grew, the role of annotators, both human and automated, became integral to the development of algorithms that rely on accurately labeled datasets.