Text Annotation Task

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A Text Annotation Task is a text processing task that is an annotation task for text annotation items.



  • (HabileData, 2024) ⇒ HabileData. (2024). “Text Annotation for NLP: A Comprehensive Guide [2024 Update].” In: [habiledata.com](https://www.habiledata.com/blog/text-annotation-for-nlp/).
    • NOTE: It explains the stages of text annotation, the importance of high-quality data, and the benefits of Human-in-the-Loop (HITL) approaches in ensuring accuracy and quality in text annotations. Key benefits include enhanced contextual understanding and the ability to handle complex data.



  • (Kili Technology, 2024) ⇒ Kili Technology. (2024). “Text annotation for NLP and document processing: a complete guide.” In: [kili-technology.com](https://kili-technology.com/data-labeling/nlp/text-annotation).
    • NOTE: It describes the process and importance of text annotation in machine learning, detailing different types of annotations such as document classification, entity recognition, and entity linking. It also emphasizes the need for high-quality annotated data to train effective NLP models.


  • (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/Text_annotation Retrieved:2020-4-12.
    • Text Annotation is the practice and the result of adding a note or gloss to a text, which may include highlights or underlining, comments, footnotes, tags, and links. Text annotations can include notes written for a reader's private purposes, as well as shared annotations written for the purposes of collaborative writing and editing, commentary, or social reading and sharing. In some fields, text annotation is comparable to metadata insofar as it is added post hoc and provides information about a text without fundamentally altering that original text.[1] Text annotations are sometimes referred to as marginalia, though some reserve this term specifically for hand-written notes made in the margins of books or manuscripts. Annotations are extremely useful and help to develop knowledge of English literature.

      This article covers both private and socially shared text annotations, including hand-written and information technology-based annotation. For information on annotation of Web content, including images and other non-textual content, see also Web annotation.

  1. Shabajee, P. and D. Reynolds. "What is Annotation? A Short Review of Annotation and Annotation Systems". ILRT Research Report No. 1053. Institute for Learning & Research Technology. Retrieved March 14, 2012.


  • (brat, 2020) ⇒ https://brat.nlplab.org/examples.html Retrieved:2020-4-12.
    • QUOTE: A variety of annotation tasks that can be performed in brat are introduced below using examples from available corpora. The examples discussed in this section have been originally created in various tools other than brat and converted into brat format. Converters for many of the original formats are distributed with brat. In the selection of examples included here, priority has been given to tasks with freely available data.



coreference resolution linking references to same entities in a text
named entity recognition identifying and labeling named entities
semantic analysis labeling predicate-argument relations
syntactic parsing analyzing constituent phrases in a sentence
part-of-speech tagging labeling words with word categories
tokenization segmenting text into words
sentence boundaries segmenting text into sentences
Fig 2.2 : Levels of Linguistic annotations.