Text Annotation Task

A Text Annotation Task is a text processing task that is an annotation task to create annotated text items.

• AKA: Text Labeling Task, Text Markup Task, Linguistic Annotation Task.
• Context:
  • Task Input: Text Dataset, Text Annotation Schema.
    • Optional: Text Annotation Guidelines, Language Model, Knowledge Base.
  • Task Output: Annotated Text Items (with text annotation labels).
    • Optional: Text Annotation Report, Inter-Annotator Agreement Score.
  • Task Performance Measure: Text Annotation Accuracy, Text Annotation F1-Score, Text Annotator Bias Measure, Text Annotation Throughput, Cohen's Kappa for Text Annotation.
  • It can typically add Text Annotation Layers to text documents.
  • It can typically apply Text Annotation Schemas for text annotation consistency.
  • It can typically create Training Data for Text Processing through text annotation examples.
  • It can typically enable Text Understanding Tasks through text annotation metadata.
  • It can typically support Text Mining Applications through text annotation structures.
  • ...
  • It can often be part of a Text Annotation Process implementing text annotation algorithms.
  • It can often require Text Annotation Quality Control through text annotation validation.
  • It can often involve Text Annotation Iterations for text annotation refinement.
  • It can often support Multi-Annotator Text Annotation for text annotation consensus.
  • ...
  • It can range from being a Manual Text Annotation Task to being an Automated Text Annotation Task, depending on its text annotation automation level.
  • It can range from being a Syntactic Text Annotation Task to being a Semantic Text Annotation Task, depending on its text annotation linguistic level.
  • It can range from being a Word-Level Text Annotation Task to being a Document-Level Text Annotation Task, depending on its text annotation granularity.
  • It can range from being a Single-Label Text Annotation Task to being a Multi-Label Text Annotation Task, depending on its text annotation label complexity.
  • It can range from being a Domain-Agnostic Text Annotation Task to being a Domain-Specific Text Annotation Task, depending on its text annotation specialization.
  • ...
  • It can be performed by Text Annotators using text annotation tools.
  • It can be supported by Text Annotation Systems implementing text annotation interfaces.
  • It can be managed through Text Annotation Projects with text annotation workflows.
  • It can be documented in Text Annotation Guidelines Documents for text annotation standardization.
  • ...
• Example(s):
  • Linguistic Text Annotation Tasks, such as:
    • Syntactic Text Annotation Tasks, such as:
      • Part-of-Speech Tagging Task for word class annotation.
      • Dependency Parsing Annotation Task for syntactic relation annotation.
      • Text Chunking Task for phrase boundary annotation.
      • Constituency Parsing Annotation Task for syntactic tree annotation.
    • Semantic Text Annotation Tasks, such as:
      • Word Sense Disambiguation Task for semantic meaning annotation.
      • Semantic Role Labeling Task for predicate-argument annotation.
      • Frame Semantic Annotation Task for semantic frame annotation.
    • Discourse Text Annotation Tasks, such as:
      • Coreference Resolution Annotation Task for reference chain annotation.
      • Discourse Relation Annotation Task for text coherence annotation.
      • Rhetorical Structure Annotation Task for discourse structure annotation.
  • Named Entity Text Annotation Tasks, such as:
    • Named Entity Recognition Task for entity mention annotation.
    • Entity Linking Task for entity reference annotation.
    • Entity Typing Task for fine-grained entity annotation.
    • Nested Entity Annotation Task for hierarchical entity annotation.
  • Information Extraction Text Annotation Tasks, such as:
    • Relation Extraction Annotation Task for entity relationship annotation.
    • Event Extraction Annotation Task for event mention annotation.
    • Temporal Expression Annotation Task for time reference annotation.
    • Attribute Extraction Annotation Task for entity property annotation.
  • Classification Text Annotation Tasks, such as:
    • Document Classification Annotation Task for document category annotation.
    • Sentiment Analysis Annotation Task for opinion polarity annotation.
    • Topic Classification Annotation Task for subject matter annotation.
    • Intent Classification Annotation Task for user intent annotation.
  • Domain-Specific Text Annotation Tasks, such as:
    • Clinical Text Annotation Task for medical concept annotation.
    • Legal Text Annotation Task for legal entity annotation.
    • Financial Text Annotation Task for financial term annotation.
    • Scientific Text Annotation Task for research concept annotation.
  • Specialized Text Annotation Tasks, such as:
    • WikiText Annotation Task for wiki markup annotation.
    • Contractual Provision Annotation Task for legal clause annotation.
    • Meta-Knowledge Annotation Task for certainty level annotation.
    • Error Annotation Task for text error identification.
  • Multi-Layer Text Annotation Tasks, such as:
    • Morphological Annotation Task for word structure annotation.
    • Phonetic Annotation Task for pronunciation annotation.
    • Prosodic Annotation Task for speech pattern annotation.
  • Collaborative Text Annotation Tasks, such as:
    • Crowdsourced Text Annotation Task for distributed annotation.
    • Expert Team Annotation Task for specialized annotation.
    • Iterative Text Annotation Task for progressive refinement.
  • Cross-Lingual Text Annotation Tasks, such as:
    • Parallel Text Annotation Task for translation alignment.
    • Code-Switching Annotation Task for language mixing annotation.
    • Cross-Lingual Entity Annotation Task for multilingual entity annotation.
  • ...
• Counter-Example(s):
  • Image Annotation Task, which annotates visual content rather than text content.
  • Audio Annotation Task, which labels sound data rather than text data.
  • Video Annotation Task, which marks video content rather than text documents.
  • DNA Annotation Task, which annotates genetic sequences rather than natural language text.
  • Subject Indexing Task, which assigns subject headings rather than creating text annotation layers.
• See: Text Processing Task, Document Annotation Task, Natural Language Processing Task, Text Annotation System, Text Annotation Process, Text Editing Task, Text Sequence Token Classification Task.

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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. 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.

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.

Fig 2.2 : Levels of Linguistic annotations.

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.

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