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.
- Context:
- Task Input: Text Dataset.
- Task Output: Annotated Text (with text labels).
- Task Requirement(s):
- Text Editing System,
- (optional) Language Model, or Knowledge Base.
- Task Performance: Text Annotator Bias Measure.
- It can (often) be a member of a Text Annotation Process (by implementing a text annotation algorithm).
- It can range from being a Manual Text Annotation Task to being an Automatic Text Annotation Task.
- It can range from being an IT-based Text Annotation Task to being a Web-based Annotation Task.
- It can range from being a Syntactic Text Annotation Task to being a Semantic Text Annotation Task.
- It can range from being a Word-Level Text Annotation Task to a being a Sentence-Level Text Annotation Task.
- It can range from being an Automated Text Annotation Task to being a Manual Text Annotation Task.
- It can range from being a Ortographic-Linguistic Annotation Task to being a [[____]].
- ...
- It can support a Corpus Annotation Task.
- ...
- Example(s):
- Counter-Example(s):
- See: Document Annotation Task, Natural Language Processing Task, Text Processing Task, Text Editing Task, WikiText, Text Error Correction Task, Text Clustering Task, Text Sequence Token Classification Task.
References
2024
- (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.
2024
- (Labellerr, 2024) ⇒ Labellerr. (2024). “The Ultimate Guide to Text Annotation: Techniques, Tools, and Best Practices.” In: [labellerr.com](https://www.labellerr.com/blog/the-ultimate-guide-to-text-annotation-techniques-tools-and-best-practices-2/).
- NOTE: It covers various techniques of text annotation, such as entity annotation and text classification, and discusses best practices in the training and maintenance of NLP models. It highlights how feedback loops and analytics are crucial for continuous improvement in intent annotation.
2024
- (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.
2020a
- (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.
- 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.
- ↑ 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.
2020b
- (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.
2015
- (Herrner & Schmidt, 2015) ⇒ http://annotation.exmaralda.org/index.php/Linguistic_Annotation Last Updated: 2015-06-30.
- QUOTE: This wiki describes tools and formats for creating and managing linguistic annotations. "Linguistic annotation" covers any descriptive or analytic notations applied to raw language data. The basic data may be in the form of time functions - audio, video and/or physiological recordings - or it may be textual. The added notations may include transcriptions of all sorts (from phonetic features to discourse structures), part-of-speech and sense tagging, syntactic analysis, "named entity" identification, co-reference annotation, and so on. The focus is on tools which have been widely used for constructing annotated linguistic databases, and on the formats commonly adopted by such tools and databases.
2009a
- (Wilcock, 2009) ⇒ Graham Wilcock. (2009). “Introduction to Linguistic Annotation and Text Analytics.” In: Synthesis Lectures on Human Language Technologies, Morgan & Claypool. DOI:10.2200/S00194ED1V01Y200905HLT003 ISBN:1598297384
- QUOTE: The current state of the art in linguistic annotation also divides the different annotation tasks into different levels, which can be arranged into a similar set of layers as shown in Figure 2.2. However, there is only an approximate correspondence between the levels of the tasks performed in practical corpus annotation work and the levels of description in linguistic theory.
| 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 |
2009b
- (Palmer, Moon & Baldridge, 2009) ⇒ Alexis Palmer, Taesun Moon, and Jason Baldridge. (2009). “Evaluating Automation Strategies in Language Documentation.” In: Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing (HLT 2009).
- QUOTE: This paper presents pilot work integrating machine labeling and active learning with human annotation of data for the language documentation task of creating interlinearized gloss text (IGT) for the Mayan language Uspanteko. The practical goal is to produce a totally annotated corpus that is as accurate as possible given limited time for manual annotation. We describe ongoing pilot studies which examine the influence of three main factors on reducing the time spent to annotate IGT: suggestions from a machine labeler, sample selection methods, and annotator expertise.
2008
- (Snow et al., 2008) ⇒ Rion Snow, Brendan O'Connor, Daniel Jurafsky, and Andrew Y. Ng. (2008). “Cheap and Fast - But is it Good?: Evaluating non-expert annotations for natural language tasks.” In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2008).
- QUOTE: Human linguistic annotation is crucial for many natural language processing tasks but can be expensive and time-consuming. We explore the use of Amazon's Mechanical Turk system, a significantly cheaper and faster method for collecting annotations from a broad base of paid non-expert contributors over the Web. We investigate five tasks: affect recognition, word similarity, recognizing textual entailment, event temporal ordering, and word sense disambiguation.