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Annotation
Annotation
Annotation
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An annotation is extra information associated with a particular point in a document or other piece of information. It can be a note that includes a comment or explanation.[1] Annotations are sometimes presented in the margin of book pages. For annotations of different digital media, see web annotation and text annotation.

Literature, grammar and educational purposes

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Practising visually

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Annotation Practices are highlighting a phrase or sentence and including a comment, circling a word that needs defining, posing a question when something is not fully understood and writing a short summary of a key section.[2] It also invites students to "(re)construct a history through material engagement and exciting DIY (Do-It-Yourself) annotation practices."[3]

Text and film annotation

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Text and film annotation is a technique that involves embedding comments or textual notes within a film. Analyzing videos is an undertaking that is never entirely free of preconceived notions, and the first step for researchers is to find their bearings within the field of possible research approaches and thus reflect on their own basic assumptions.[4] Annotations can take part within the video, and can be used when the data video is recorded. It is being used as a tool in text and film to write one's thoughts and emotion into the markings.[2] In any number of steps of analysis, it can also be supplemented with more annotations. Anthropologists Clifford Geertz calls it a "thick description." This can give a sense of how useful annotation is, especially by adding a description of how it can be implemented in film.[4]

Medieval marginalia

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Main article: Marginalia
Annotation of a 15th century text

Marginalia are notes or drawings in the margins of manuscripts. Readers commonly wrote notes in the margins of books to enhance the understanding of later readers.

Textual scholarship

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Main article: Text annotation

Textual scholarship is a discipline that often uses the technique of annotation to describe or add additional historical context to texts and physical documents to make it easier to understand.[5]

Student uses

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Passages marked with a highlighter pen

Students often highlight passages in books in order to actively engage with the text. Students can use annotations to refer back to key phrases easily, or add marginalia to aid studying and finding connections between the text and prior knowledge or running themes.[6]

Annotated bibliographies add commentary on the relevance or quality of each source, in addition to the usual bibliographic information that merely identifies the source.

Students use Annotation not only for academic purposes, but interpreting their own thoughts, feelings, and emotions.[2]

Mathematical expression annotation

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Mathematical expressions (symbols and formulae) can be annotated with their natural language meaning. This is essential for disambiguation, since symbols may have different meanings (e.g., "E" can be "energy" or "expectation value", etc.).[7][8] The annotation process can be facilitated and accelerated through recommendation, e.g., using the "AnnoMathTeX" system that is hosted by Wikimedia.[9][10][11]

Learning and instruction

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From a cognitive perspective, annotation has an important role in learning and instruction. As part of guided noticing it involves highlighting, naming or labelling and commenting aspects of visual representations to help focus learners' attention on specific visual aspects. In other words, it means the assignment of typological representations (culturally meaningful categories), to topological representations (e.g. images).[12] This is especially important when experts, such as medical doctors, interpret visualizations in detail and explain their interpretations to others, for example by means of digital technology.[13] Here, annotation can be a way to establish common ground between interactants with different levels of knowledge.[14] The value of annotation has been empirically confirmed, for example, in a study which shows that in computer-based teleconsultations the integration of image annotation and speech leads to significantly improved knowledge exchange compared with the use of images and speech without annotation.[15]

On YouTube

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Annotations were removed on January 15, 2019, from YouTube after around a decade of service.[16] They had allowed users to provide information that popped up during videos, but YouTube indicated they did not work well on small mobile screens, and were being abused.

Software and engineering

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Text documents

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Main article: Text annotation

Markup languages like XML and HTML annotate text in a way that is syntactically distinguishable from that text. They can be used to add information about the desired visual presentation, or machine-readable semantic information, as in the semantic web.[17]

Tabular data

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This includes CSV and XLS. The process of assigning semantic annotations to tabular data is referred to as semantic labelling. Semantic Labelling is the process of assigning annotations from ontologies to tabular data.[18][19][20][21] This process is also referred to as semantic annotation.[22][21] Semantic Labelling is often done in a (semi-)automatic fashion. Semantic Labelling techniques work on entity columns,[21] numeric columns,[18][20][23][24] coordinates,[25] and more.[25][24]

Semantic labelling techniques

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There are several semantic labelling types which utilises machine learning techniques. These techniques can be categorised following the work of Flach[26][27] as follows: geometric (using lines and planes, such as Support-vector machine, Linear regression), probabilistic (e.g., Conditional random field), logical (e.g., Decision tree learning), and Non-ML techniques (e.g., balancing coverage and specificity[21]). Note that the geometric, probabilistic, and logical machine learning models are not mutually exclusive.[26]

Geometric techniques
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Pham et al.[28] use Jaccard index and TF-IDF similarity for textual data and Kolmogorov–Smirnov test for the numeric ones. Alobaid and Corcho[20] use fuzzy clustering (c-means[29][30]) to label numeric columns.

Probabilistic techniques
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Limaye et al.[31] uses TF-IDF similarity and graphical models. They also use support-vector machine to compute the weights. Venetis et al.[32] construct an isA database which consists of the pairs (instance, class) and then compute maximum likelihood using these pairs. Alobaid and Corcho[33] approximated the q-q plot for predicting the properties of numeric columns.

Logical techniques
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Syed et al.[34] built Wikitology, which is "a hybrid knowledge base of structured and unstructured information extracted from Wikipedia augmented by RDF data from DBpedia and other Linked Data resources."[34] For the Wikitology index, they use PageRank for Entity linking, which is one of the tasks often used in semantic labelling. Since they were not able to query Google for all Wikipedia articles to get the PageRank, they used Decision tree to approximate it.[34]

Non-ML techniques
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Alobaid and Corcho[21] presented an approach to annotate entity columns. The technique starts by annotating the cells in the entity column with the entities from the reference knowledge graph (e.g., DBpedia). The classes are then gathered and each one of them is scored based on several formulas they presented taking into account the frequency of each class and their depth according to the subClass hierarchy.[35]

Semantic labelling common tasks

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Here are some of the common semantic labelling tasks presented in the literature:

Entity linking and disambiguation
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This is the most common task in semantic labelling. Given a text of a cell and a data source, the approach predicts the entity and link it to the one identified in the given data source. For example, if the input to the approach were the text "Richard Feynman" and a URL to the SPARQL endpoint of DBpedia, the approach would return "http://dbpedia.org/resource/Richard_Feynman", which is the entity from DBpedia. Some approaches use exact match.[21] while others use similarity metrics such as Cosine similarity[31]

Subject column identification
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The subject column of a table is the column that contain the main subjects/entities in the table.[18][27][32][36][37] Some approaches expects the subject column as an input[21] while others predict the subject column such as TableMiner+.[37]

Column data-type detection
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Columns types are divided differently by different approaches.[27] Some divide them into strings/text and numbers[20][28][38][24] while others divide them further[27] (e.g., Number Typology,[18] Date,[34][32] coordinates[39]).

Relation prediction
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The relation between Madrid and Spain is "capitalOf".[40] Such relations can easily be found in ontologies, such as DBpedia. Venetis et al.[32] use TextRunner[41] to extract the relation between two columns. Syed et al.[34] use the relation between the entities of the two columns and the most frequent relation is selected.

Gold standards

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T2D[42] is the most common gold standard for semantic labelling. Two versions exists of T2D: T2Dv1 (sometimes are referred to T2D as well) and T2Dv2.[42] Another known benchmarks are published with the SemTab Challenge.[43]

Source control

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The "annotate" function (also known as "blame" or "praise") used in source control systems such as Git, Team Foundation Server and Subversion determines who committed changes to the source code into the repository. This outputs a copy of the source code where each line is annotated with the name of the last contributor to edit that line (and possibly a revision number). This can help establish blame in the event a change caused a malfunction, or identify the author of brilliant code.

Programming

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Java annotations

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Main article: Java annotation

A special case is the Java programming language, where annotations can be used as a special form of syntactic metadata in the source code and can be manipulated upon with reflective programming.[44] Classes, methods, variables, parameters and packages may be annotated. The annotations can be embedded in class files generated by the compiler and may be retained by the Java virtual machine and thus influence the run-time behaviour of an application. It is possible to create meta-annotations out of the existing ones in Java.[45]

Other languages, such as C#, have a similar feature called "attributes". C++ features "attributes" which allow the programmer to give indications to the compiler[46], and C++26 introduces reflection annotations similar to Java annotations.[47]

Image annotation

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Main article: Automatic image annotation

Automatic image annotation is used to classify images for image retrieval systems.[48]

Computational biology

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Main article: DNA annotation

Since the 1980s, molecular biology and bioinformatics have created the need for DNA annotation. DNA annotation or genome annotation is the process of identifying the locations of genes and all of the coding regions in a genome and determining what those genes do. An annotation (irrespective of the context) is a note added by way of explanation or commentary. Once a genome is sequenced, it needs to be annotated to make sense of it.[49]

Digital imaging

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In the digital imaging community the term annotation is commonly used for visible metadata superimposed on an image without changing the underlying master image, such as sticky notes, virtual laser pointers, circles, arrows, and black-outs (cf. redaction).[50]

In the medical imaging community, an annotation is often referred to as a region of interest and is encoded in DICOM format.

Other uses

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Law

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In the United States, legal publishers such as Thomson West and Lexis Nexis publish annotated versions of statutes, providing information about court cases that have interpreted the statutes. Both the federal United States Code and state statutes are subject to interpretation by the courts, and the annotated statutes are valuable tools in legal research.[51]

Linguistics

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Main article: Text annotation § Linguistic annotation

One purpose of annotation is to transform the data into a form suitable for computer-aided analysis. Prior to annotation, an annotation scheme is defined that typically consists of tags. During tagging, transcriptionists manually add tags into transcripts where required linguistical features are identified in an annotation editor. The annotation scheme ensures that the tags are added consistently across the data set and allows for verification of previously tagged data.[52] Aside from tags, more complex forms of linguistic annotation include the annotation of phrases and relations, e.g., in treebanks. Many different forms of linguistic annotation have been developed, as well as different formats and tools for creating and managing linguistic annotations, as described, for example, in the Linguistic Annotation Wiki.[53]

See also

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Look up annotation in Wiktionary, the free dictionary.

References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Annotation

View on Grokipedia
from Grokipedia
Annotation is the process of adding supplementary information, such as notes, comments, explanations, or metadata, to a like text, , or sequences to enhance comprehension, , or functionality. This practice, which dates back to ancient scholarly traditions of in manuscripts, serves to clarify ambiguities, provide , or link to related across diverse fields. Annotations can take various forms, including handwritten marginal notes, digital tags, or structured labels, and are essential for knowledge dissemination and interpretation. In literary and textual studies, annotation involves augmenting original works with interpretive commentary to aid readers in understanding nuances, historical context, or , often appearing as , endnotes, or inline highlights. This method fosters active engagement with the material, enabling critical analysis and personal reflection, and has evolved with digital tools to support collaborative and hyperlinked references. Scholarly editions of classic texts frequently rely on extensive annotations to reconstruct variant readings or cultural significances. In and , annotation refers to the identification and functional of genomic elements, such as genes and regulatory regions, within sequence, typically inferred from sequence similarity or experimental . This process is crucial for translating raw genomic data into biological insights, supporting research in areas like disease genetics and , and is often automated using computational pipelines while requiring manual curation for accuracy. High-quality genome annotations underpin databases like Ensembl and , facilitating and . In and , annotation encompasses the labeling of datasets with descriptive tags or categories to train algorithms, enabling models to recognize patterns in text, images, or audio. For instance, in , annotations create corpora for tasks like , while in , they involve bounding boxes around objects. The demand for annotated data has surged with AI advancements, leading to specialized tools and platforms, though challenges like inter-annotator agreement persist.

Overview and History

Definition and Etymology

Annotation refers to the process of adding explanatory notes, labels, or metadata to text, images, data, or other media to clarify, interpret, or enhance understanding. This practice involves associating additional information with specific points in the source material, often providing context, commentary, or analysis that aids comprehension without altering the original content. The term "annotation" derives from the Latin annotare, meaning "to note down" or "to mark," a combination of ad- ("to") and notare ("to note" or "to mark"). It entered English in the mid-15th century via annotation, initially referring to written comments or remarks in manuscripts, and has since evolved to encompass digital tags and metadata in modern contexts. A key distinction exists between annotation and mere highlighting: while highlighting visually emphasizes portions of text without adding interpretive content, annotation incorporates explanatory or analytical notes to deepen . Similarly, annotation differs from citation, as it actively explains or contextualizes referenced material rather than simply identifying its source. For instance, a simple footnote in a printed might clarify an archaic term, whereas complex layered annotations in digital tools, such as those in collaborative platforms, allow multiple users to add interconnected comments and tags to shared documents.

Historical Development

The practice of annotation traces its origins to and , where scholars inscribed notes on scrolls to aid in and interpretation. In the , annotations known as scholia emerged as marginal or interlinear comments on literary works, compiling explanations from earlier commentators to clarify difficult passages, linguistic variations, and historical contexts. A prominent example is the scholia on Homer's and , with traditions dating back to at least the 3rd century BCE but significant compilations appearing by the 2nd century BCE, reflecting the scholarly efforts of Alexandrian critics like and Aristarchus to establish authoritative texts. During the medieval period, annotation practices evolved significantly in monastic scriptoria, where glosses—brief explanatory notes—and interlinear annotations became essential tools for copying and interpreting sacred and classical texts. From the 8th to the 12th centuries, these notes facilitated the preservation and transmission of knowledge amid widespread illiteracy, often inserted between lines or in margins to translate Latin into vernacular languages or elucidate theological points. The (c. 780–900 CE), under Charlemagne's reforms, marked a peak in this development, as scriptoria in centers like Tours and produced annotated manuscripts that standardized works, ensuring their survival through meticulous glossing. A key early figure was (c. 560–636 CE), whose provided etymological annotations deriving word origins to compile an encyclopedic of knowledge, influencing medieval scholarship by linking language to broader cultural and natural histories. The transition to the early , catalyzed by the invention of the around 1440, transformed annotation from a labor-intensive tradition into a reproducible feature of printed books, with now standardized alongside the main text to guide readers. Humanist scholars embraced this medium to revive classical learning, as seen in Desiderius Erasmus's (1516), which included extensive annotations on the Greek critiquing translations and advocating philological accuracy. By the , philological editions of classical texts incorporated layered annotations for rigorous , such as commentaries on ' works that analyzed variants, metrics, and historical allusions, reflecting the era's emphasis on scientific . Throughout this evolution, annotation shifted from ephemeral oral commentaries—recited in ancient rhetorical schools—to persistent written forms, propelled by rising literacy rates in medieval and the printing press's capacity for mass dissemination in the . This progression not only democratized interpretive practices but also embedded annotations as integral to textual authority, bridging scholarly discourse across eras.

General Types

Annotations are broadly classified by their purpose into explanatory, critical, descriptive, and procedural types, providing a foundational framework for understanding their role across contexts. Explanatory annotations clarify or expand upon the original content, offering definitions, context, or supplementary details to enhance comprehension without altering the primary meaning. For instance, a footnote explaining a historical term in a document falls into this category, aiding readers in grasping nuanced ideas. Critical annotations, by contrast, involve evaluation and analysis, assessing the content's validity, biases, or implications to foster deeper critique. These often appear in scholarly reviews, where the annotator judges the source's reliability or contributions. Descriptive annotations focus on cataloging attributes through metadata like tags, summaries, or categorizations, enabling organization and retrieval without interpretive judgment. Procedural annotations guide practical actions or sequences, such as step-by-step instructions in manuals or workflow directives, emphasizing functionality over analysis. Structurally, annotations vary in placement and integration to suit different presentation needs. Inline annotations are embedded directly within the primary text, such as parenthetical asides that interrupt the flow minimally for immediate . Marginal annotations occupy the sides or edges of the content, allowing commentary without disrupting the main , as seen in traditional margins. Endnotes compile annotations at the document's conclusion, preserving textual continuity while providing consolidated . In digital environments, hyperlinked annotations enable overlays or external connections, where clicking reveals additional layers of information without cluttering the base material. These types manifest across media independently of specific domains. For example, geographical labels on 16th-century maps, such as those in the Geographic Reports of , function as descriptive annotations by identifying locations and features for navigational clarity. Similarly, timestamps in podcasts serve as procedural annotations, marking key segments to direct listeners to relevant audio portions efficiently. The evolution of annotation types reflects technological shifts, transitioning from static, paper-based implementations—limited to fixed ink or print—to dynamic, interactive forms in applications that support real-time and . Classifications often hinge on criteria like purpose (e.g., clarification versus guidance), medium (e.g., text versus audio), and (e.g., passive reading aids versus editable digital notes), ensuring adaptability to user needs. A prevalent misconception equates all metadata with annotation; however, navigational elements like indexes primarily facilitate access rather than provide interpretive or guiding insights, distinguishing them from true annotations.

Annotations in Literature, Education, and Media

Literary and Textual Annotations

Literary and textual annotations serve to enhance readers' comprehension of complex or archaic , provide essential for historical, cultural, or literary allusions, and facilitate scholarly debate over interpretive possibilities. In scholarly editing, these annotations clarify obscure terms, explain narrative events, and illuminate references that might otherwise elude modern audiences, thereby bridging temporal gaps between original composition and contemporary reading. Variorum editions, which compile variant readings and commentaries from multiple sources, exemplify this purpose by presenting diverse interpretations side by side, allowing readers to engage with evolving scholarly consensus. Techniques for literary annotations include glossaries to define grammatical structures and vocabulary, as well as or endnotes for translations and explanatory details. In editions of Shakespeare's works, such as those derived from the 1623 , annotations often feature tiered levels: basic notes for immediate clarification of Elizabethan syntax and wordplay, advanced discussions of staging cues or allusions, and discursive essays on interpretive controversies. For instance, the Shakespeare Editions employ glossaries for recurring terms and citing parallel texts from the period, like the for biblical references, to maintain fidelity to the original while aiding accessibility. These methods ensure that annotations support rather than overshadow the primary text. Textual scholarship relies on annotations to collate manuscript variants and reconstruct authoritative texts through methods like stemmatics, which traces the genealogical relationships among copies by identifying shared errors. Developed in the by Karl Lachmann and formalized by Paul Maas, stemmatics involves recensio (classifying manuscripts) and emendatio (selecting optimal readings), enabling editors to approximate an free from later corruptions. This approach has been pivotal in establishing reliable editions of works like Chaucer's Tales, where annotations stemmatic trees to justify editorial choices. In modern literary practice, hypertext annotations in e-books allow for dynamic, linked notes that expand on allusions or variants without cluttering the page, offering users customizable access to resources like dictionaries or related manuscripts. Critical editions such as the Norton Anthology series integrate annotations with contextual documents and selected essays, providing historical background and critical perspectives to deepen analysis of texts like those by or . These digital and print formats enhance interpretive flexibility, as seen in variorum projects that embed links for comprehensive exploration. Challenges in literary annotation include balancing fidelity to with the inevitable influence of editorial , particularly in interpretive notes that may impose contemporary values. Eighteenth-century commentaries, such as those on Pope's satirical , often feature dated annotations that obscure topical allusions due to shifting cultural contexts, complicating efforts to remain neutral. Editors must therefore document their methodologies explicitly to mitigate , ensuring annotations serve scholarly transparency rather than personal agendas.

Educational and Instructional Uses

Annotations play a central role in pedagogical practices by fostering active reading strategies that enhance student engagement and comprehension. In approaches such as Socratic seminars, students annotate texts prior to discussions to identify key evidence and generate open-ended questions, enabling them to reference specific passages during collaborative dialogues that promote and deeper understanding of the material. Similarly, in writing workshops, teacher annotations provide targeted feedback on student drafts, highlighting strengths in structure and suggesting revisions for clarity, which guides iterative improvements and builds metacognitive awareness of writing processes. Student practices involving annotations encourage interactive engagement with texts, such as highlighting key concepts, posing marginal questions, and summarizing ideas in their own words, which transform passive reading into an active process that aids recall and analysis. Digital tools, like highlighters in platforms, allow students to layer notes on submissions, facilitating and without altering original documents. These methods support skill development by prompting students to monitor their comprehension in real time. In , teachers use annotations to scaffold learning, particularly in error correction on assignments where inline comments explain misconceptions and model correct reasoning. For language learners, such as in ESL contexts, glosses—brief annotations providing definitions or examples—integrated into digital texts via (CALL) tools significantly boost acquisition and retention; for instance, video-enhanced glosses yielded up to 80% post-test accuracy in idiom recognition among intermediate EFL students, outperforming text-only formats. Evidence from underscores the benefits of annotations for retention and achievement. A study of eighth-grade students in found that those using annotation strategies during reading showed higher post-test scores (mean of 80 versus 79 for traditional methods), with qualitative feedback indicating improved and . Metacognitive annotations, which involve reflective questioning, have been shown to improve retention through multimodal formats that combine text with visuals. Teacher-provided annotations in video lessons also increased behavioral and cognitive , with 86% of students reporting better comprehension and retention. Modern adaptations leverage collaborative annotations in online courses to extend these benefits. Platforms like , integrated as Moodle plugins, enable shared commenting on readings, fostering community and equity; one implementation at correlated with a 24% rise in retention rates for gateway English courses by boosting participation and sense of belonging. However, over-reliance on annotations can limit independent if students depend excessively on pre-provided notes rather than generating their own insights, potentially reducing deeper text processing.

Marginalia and Visual Practices

Marginalia refers to the handwritten notes, doodles, symbols, or illustrations added in the margins of books or manuscripts, serving as personal annotations that interact with the primary text. These markings can range from brief comments and glosses to elaborate drawings, often reflecting the reader's immediate reactions, interpretations, or creative impulses. In medieval bestiaries from the , such as the lavish illustrations in French royal prayer books, marginalia included fantastical depictions of animals like or knights battling snails, blending textual descriptions with visual commentary to moralize or amuse. Historically, held significant roles in medieval scholarship and expression, particularly among friars who used it for theological commentary and subtle rebellion against orthodox texts. Friars' glosses on works like the provided interpretive layers to biblical and philosophical writings, allowing mendicants to expand on doctrine while navigating ecclesiastical constraints, as seen in 13th-century compendia. These annotations fostered critical dialogue, sometimes challenging central narratives through or alternative viewpoints. In the , artists like elevated marginalia to a visual art form, incorporating intricate drawings and annotations in prayer books to enhance devotional texts, as evidenced by his 1515 marginal designs in a manuscript that integrated pen-and-ink illustrations with printed pages. Visual practices extended into pedagogical tools, particularly in exercises where diagrams aided comprehension of complex structures. Medieval manuscripts, drawing from Priscian's Institutiones grammaticae, featured branching diagrams and tree-like schematics in margins to parse sentences and illustrate rhetorical concepts, facilitating in monastic and scholastic settings. By the early , these traditions influenced proto-annotation methods in film, where storyboarding emerged as sequential visual sketches to plan shots and narratives, originating with artists like in animated sequences that prefigured cinematic annotation. The cultural impact of lies in its revelation of readers' inner worlds, with psychological studies analyzing these marks to infer traits and cognitive styles. For instance, analyses around 2015 examined annotations in educational contexts to model learners' traits like or based on annotation patterns, highlighting marginalia's role as a spontaneous into . Libraries worldwide preserve these artifacts, such as in Emory University's archival collections, to study reader responses and historical , underscoring marginalia's value as cultural testimony. With the rise of and , traditional declined as books became commodities less amenable to personalization, shifting toward ephemeral digital in e-readers and apps that mimic margin writing. Yet, revival efforts persist, evident in the annotated manuscripts of , whose marginal revisions in works like The Trial reveal his iterative creative process, now digitized for scholarly access and inspiring modern annotated editions.

Media and Digital Platform Annotations

In film production, annotations have long served as essential tools for script breakdowns, where directors add detailed notes on staging, camera angles, and performance directions to guide the creative process. These practices trace back to early Hollywood, particularly in the 1930s, when standardized screenplay formats emerged amid the transition to sound films, allowing directors to annotate scripts for efficient collaboration with crews. For instance, during this era, multiple-language versions of films were produced with overlaid subtitles that included glosses to explain cultural references unfamiliar to international audiences, such as idiomatic expressions or historical allusions in multilingual adaptations of MGM and Paramount pictures. In digital video platforms, timestamping has become a core annotation method to highlight key moments, enabling viewers to navigate content efficiently; on , creators add timecodes in video descriptions to generate automatic chapters, improving for long-form videos like tutorials. Community-driven annotations proliferated on following the feature's introduction in June 2008, which allowed users to overlay interactive text, links, and corrections on videos, fostering collaborative enhancements in educational tutorials where viewers added clarifications or resources. However, deprecated the annotation tool in due to declining usage—dropping 70% amid rising mobile traffic incompatibility—and fully removed existing annotations by January 15, 2019, replacing them with cards and end screens for better cross-device compatibility. Social media platforms extend annotations through tags and hashtags, which function as metadata to boost virality by categorizing videos and surfacing them in algorithmic feeds; for example, trending hashtags like #Viral or #FYP on and can exponentially increase views by aligning content with popular searches. Yet, these user-generated annotations pose challenges, including the spread of , as has been identified as a major conduit for through unverified overlays and comments that amplify falsehoods without adequate . Conversely, annotations enhance , with closed captions serving as synchronized text annotations that benefit deaf or hard-of-hearing users by conveying and sound cues, while also aiding non-native speakers and improving overall comprehension in over 100 studies on video retention. Recent trends as of 2025 reflect a shift toward AI-assisted annotations in short-form video platforms, where TikTok's tools like AI Outline generate automated titles, hashtags, and content structures from prompts, streamlining creator workflows for layered commentary in videos. This integration supports the growth of educational vlogs, which emphasize expert-driven formats with multi-layered elements such as on-screen text, voiceovers, and interactive prompts to deliver in-depth tutorials, capitalizing on YouTube's prioritization of engaging, value-added content amid rising demand for how-to videos.

Annotations in Computing and Software Engineering

Programming and Source Code Annotations

In programming and source code annotations, developers attach metadata to code elements such as classes, methods, and variables to describe behavior, enforce constraints, or facilitate processing during compilation, runtime, or analysis. These annotations serve purposes like documentation, error prevention, and automation of repetitive tasks, evolving from traditional non-executable comments—which merely provide human-readable notes—into declarative, machine-readable constructs that can influence code execution or generation, particularly prominent since the early 2000s. Unlike comments, which are ignored by compilers and interpreters, annotations enable runtime reflection for inspecting and modifying program behavior dynamically. Annotations appear in various languages beyond Java, such as Python's decorators for modifying functions and C# attributes for metadata on code elements. Java annotations, standardized by JSR-175 in 2002 and introduced in 5 (released in 2004), provide a syntax for defining metadata using the @ symbol followed by an interface name, such as @interface for custom annotation types. For instance, the predefined @Override annotation, also from 5, indicates that a method intends to override a superclass method, allowing the to verify the override and prevent errors like accidental overloading. Annotation processors, enhanced in 6 via JSR-269, scan and process these metadata at to generate or validate structures; in the , annotations like @Autowired enable by automatically wiring beans based on type matching, reducing boilerplate XML configuration. This framework, widely adopted since its 2.5 release in 2007, uses such annotations to declare components (@Component) and inject dependencies, streamlining enterprise development. In version control systems, source code annotations appear in commit messages and tools like Git's blame feature, which annotates each line of a file with the commit hash, author, and date of its last modification to track changes and accountability. Git commit messages, following conventions such as those in the Linux kernel, include structured tags like "Signed-off-by:" to certify authorship and compliance with development policies, or "Fixes:" to link bug fixes to originating commits, aiding maintenance in large projects. The Linux kernel repository exemplifies this, where a vast majority of commits include such tags to enforce review processes and trace evolutions in its million-plus lines of code. These practices, rooted in Git's design since 2005, extend annotations beyond code files to repository metadata, enhancing collaboration in open-source ecosystems. The benefits of annotations include improved error detection—such as compile-time checks via @Override—and runtime efficiency through reflection, as seen in frameworks like Spring where annotations streamline configuration compared to XML alternatives. In open-source projects like the , tag-based commit annotations facilitate automated bisecting for , shortening resolution times for regressions. Overall, these mechanisms promote maintainable, while bridging human intent with automated tooling.

Text and Document Annotations

Text and document annotations in involve the systematic addition of metadata, tags, or comments to non-executable text files to enhance their , searchability, and in software environments. These annotations facilitate by applications such as search engines, systems, and collaborative platforms, enabling features like entity extraction, semantic enrichment, and without altering the core content. Unlike interpretive annotations in contexts, these focus on machine-readable enhancements for digital workflows. Key techniques include (NER), which identifies and tags specific entities such as persons, organizations, or locations within text to support and analysis. NER employs models, often based on or deep neural networks, to classify spans of text accurately, achieving F1 scores above 90% on standard benchmarks like CoNLL-2003 for English texts. Another prominent method is XML-based markup, exemplified by the (TEI), a standard for encoding texts that allows hierarchical tagging of linguistic features, structural elements, and metadata in XML format. TEI enables detailed annotation of texts for scholarly analysis, such as marking variants or rhetorical structures, and is widely adopted by digital libraries for interoperability. Tools for implementing these annotations range from commercial software to open-source solutions tailored for specific domains. provides built-in commenting features that allow users to add , highlights, and text edits directly to PDF documents, supporting collaborative review and export to annotated formats. For linguistic corpora, the open-source Brat Rapid Annotation Tool offers a web-based interface for creating and relation annotations, emphasizing speed and through visual markup on text spans, and has been adopted in projects involving large-scale NLP datasets. Applications of text annotations extend to version tracking in collaborative documents, where tools like use suggestion modes to track changes, comments, and proposed edits, maintaining a history of modifications for team-based authoring. In accessibility contexts, annotations such as alternative text (alt text) for embedded images in PDFs ensure compatibility, complying with standards like WCAG by describing visual content in textual form to support users with visual impairments. Standards governing these practices include the ISO 24617 series, part of the Semantic Annotation Framework (SemAF), which defines a core model for annotating semantic roles, events, and relations in texts to promote consistency across language resources. ISO 23081 provides principles for metadata, ensuring annotations capture essential attributes like creation date and authorship for long-term document preservation. Challenges in these standards arise particularly with multilingual texts, where variations in script, morphology, and cultural nuances complicate automated tagging, often resulting in lower accuracy rates in low-resource languages compared to English. As of 2025, recent developments feature the integration of large language models (LLMs) for auto-annotation in word processors, enhancing efficiency through automated content assistance such as summaries, edits, and tagging suggestions. For instance, Word's Copilot, powered by LLMs, generates summaries and edits in real-time to support . Similarly, incorporates Gemini AI to draft, rewrite, and suggest content improvements, streamlining collaborative workflows. These advancements, building on frameworks like ISO 24617, reduce manual effort in annotation tasks, as demonstrated in LLM-assisted pipelines for text corpora.

Data Annotation Techniques

Data annotation techniques encompass a range of methods for labeling structured data, such as tabular datasets and images, to prepare them for applications. These techniques aim to assign meaningful labels that capture semantic relationships and enable model training, often involving human annotators or automated processes to ensure accuracy and scalability. In tabular data annotation, semantic labeling identifies the meaning of data elements, such as treating column headers as entities to facilitate tasks like entity resolution, where records are matched across datasets to resolve duplicates or inconsistencies. For instance, tools and frameworks like Kepler-aSI automate semantic annotations by linking tabular columns to real-world concepts from ontologies, improving for downstream analysis. Key techniques for efficient annotation include and . Crowdsourcing platforms like distribute labeling tasks to a global workforce, enabling rapid annotation of large datasets at low cost, as demonstrated in early applications for image and object labeling where workers provided high-quality annotations via simple interfaces. minimizes the need for extensive labeling by iteratively selecting the most informative data points for annotation based on model uncertainty, thereby reducing manual effort while improving training efficiency; this approach has been shown to cut annotation requirements by up to 50% in pipelines. For image data, annotation techniques focus on spatial localization and segmentation to support tasks. Bounding boxes outline object locations with rectangular coordinates, while segmentation provides pixel-level masks for precise boundaries; the COCO dataset, introduced in 2014, standardized these methods with annotations for over 330,000 images, including 1.5 million object instances across 80 categories, serving as a benchmark for and instance segmentation. Tools like LabelImg facilitate these annotations through user-friendly graphical interfaces that output formats compatible with frameworks such as PASCAL VOC, allowing annotators to draw boxes and assign labels efficiently. In the context of AI and machine learning, data annotation prepares datasets for supervised learning by creating labeled splits, such as the conventional 80/20 ratio for training and testing sets, which ensures robust model evaluation without overfitting. Gold standard datasets like exemplify this, featuring over 14 million annotated images with labels for 21,841 categories, crowdsourced via to enable large-scale benchmarks that have driven advances in . Common tasks include , where items are categorized (e.g., identifying object types in images), and relation extraction, which identifies connections between entities in structured data like tables to build knowledge graphs. Challenges in these techniques revolve around consistency, with inter-annotator agreement measured by statistic, where values above 0.8 indicate near-perfect reliability and are considered ideal for high-stakes applications to minimize labeling errors. As of 2025, synthetic data generation using Generative Adversarial Networks (GANs) has emerged to address manual annotation bottlenecks, producing realistic labeled datasets that significantly reduce reliance on human labor in scenarios with constraints or data scarcity, while maintaining model performance comparable to real annotations.

Annotations in Science, Law, and Linguistics

Biological and Scientific Annotations

In biological and scientific contexts, annotations refer to the process of assigning descriptive metadata to genomic sequences, proteins, experimental results, and other to elucidate their functions, structures, and relationships. This practice is foundational in , enabling researchers to interpret complex datasets from high-throughput technologies and advance fields like and . Annotations bridge raw to biological knowledge, supporting hypothesis generation, model building, and therapeutic development. A cornerstone of genomic annotation is the (GO) initiative, which provides a of terms organized into hierarchies for molecular functions, biological processes, and cellular components. These GO terms are systematically applied to genes and gene products across species, allowing for standardized functional predictions and enrichment analyses that reveal overrepresented pathways in datasets. For instance, GO annotations facilitate the interpretation of differentially expressed genes in disease studies by linking them to specific biological roles. Complementing this, the Ensembl project, initiated in 1999, offers an automated platform for annotating eukaryotic genomes through integrative pipelines that combine predictions, homology-based alignments, and experimental evidence to delineate gene models, regulatory elements, and variants. Protein functional annotation techniques further extend these efforts, with databases like serving as comprehensive repositories that detail sequence features, post-translational modifications, interactions, and evolutionary conservation. UniProt's hybrid approach integrates manual expert curation for high-confidence entries with rule-based automation for scalability, ensuring annotations reflect both experimental validations and computational inferences. Phylogenetic markers, such as orthologous genes or conserved sequence motifs (e.g., 16S rRNA in ), are annotated to reconstruct evolutionary trees, informing and functional divergence; tools like PhyloPhlAn 3.0 exemplify this by processing annotated proteomes to generate robust phylogenies with minimal user input. In applications such as , pathway annotations map annotated genes and proteins onto interaction networks, identifying bottlenecks or hubs amenable to pharmacological intervention. For example, annotations in resources like Reactome highlight dysregulated signaling cascades in cancer, guiding target selection and repurposing efforts. However, high-throughput data from next-generation sequencing (NGS) poses significant challenges, including fragmented assemblies, repetitive regions, and error-prone variant detection, which automated pipelines often mishandle, leading to incomplete or inaccurate annotations. Standards like those from the (HGNC) mitigate such issues by enforcing unique, stable symbols for human genes—covering over 42,000 loci—to ensure consistency across global databases and reduce errors in collaborative research. Accuracy remains a key concern, with automated annotations prone to higher error rates due to reliance on sequence similarity; for GO terms, similarity-based (in silico) annotations exhibit up to 49% errors, while experimental or manual evidence yields 13-18%, demonstrating a substantial improvement from human oversight. Recent advances as of 2025 include CRISPR-specific annotation pipelines that incorporate editing efficiency metrics and off-target profiling, such as those analyzing GuideSeq data to annotate indel spectra and epigenetic changes post-editing. Additionally, AI integration in variant calling has enhanced annotation precision, with models like DeepVariant leveraging convolutional neural networks on NGS reads to outperform traditional methods, achieving F1 scores above 0.95 for single-nucleotide variants and enabling more reliable functional assignments in clinical genomics. Furthermore, models have improved gene structure annotation by providing accurate protein structure predictions that support functional inferences, as demonstrated in 2025 studies on and genomes. Legal annotations refer to explanatory notes, summaries, and interpretive commentaries added to legal texts such as case reports, statutes, and treaties to aid in understanding and application. These annotations serve primary purposes including providing headnotes—concise summaries of key legal principles or facts in judicial opinions—and facilitating cross-references between related provisions in statutory codes. For instance, in U.S. case reports, headnotes are editorial summaries written by publishers like or , appearing at the beginning of opinions to outline the court's rulings on specific issues. In statutory contexts, annotations in the United States Code Annotated (U.S.C.A.) by include cross-references to related statutes, court decisions interpreting the provision, and secondary sources, enabling researchers to trace legislative intent and judicial evolution. Historically, legal annotations trace back to practices in , where marginal notes appeared in early reports known as Year Books, which documented court proceedings from the late 13th to 16th centuries. These Year Books, covering cases from 1268 to 1535, included brief notations in the margins to highlight procedural points or rulings, serving as rudimentary aids for practitioners in an era without standardized reporting. In the , statutory supplements have evolved to provide ongoing annotations, such as notes on amendments, court interpretations, and historical context, ensuring that codes like the U.S. Code remain dynamic tools for legal analysis. Key techniques in creating legal annotations involve digesting precedents, where editors distill case holdings into topical summaries organized under subject headings and key numbers for efficient retrieval. This digesting process, pioneered by West Publishing, classifies legal issues into an alphabetical outline of over 400 topics, allowing users to locate analogous cases through headnotes linked to these categories. Digital tools like LexisNexis enhance this by offering hyperlinked annotations in platforms such as U.S.C.S., where notes connect directly to full case texts, statutes, or secondary materials, streamlining research in annotated codes. Challenges in legal annotations include potential editorial , where compilers' interpretations in notes may subtly influence perceptions of , as seen in studies of implicit biases affecting legal and selection. Such biases can arise from unconscious in summarizing cases, complicating objective interpretation. Additionally, annotated texts play a vital role in , with resources like the Constitution Annotated providing interpretive essays on U.S. constitutional provisions to teach students about judicial doctrines and historical applications. Prominent examples include annotations in , which accompany definitions with references to , statutes, and historical usage to illustrate term evolution, such as cross-links to digest topics for practical application. In , conventions often feature explanatory protocols as annotations, providing interpretive guidance on provisions; for instance, the Vienna Convention on the Law of Treaties includes commentaries elucidating rules on reservations and interpretations.

Linguistic Annotations

Linguistic annotations involve the systematic labeling of linguistic data to capture structural, syntactic, semantic, or phonetic properties of , enabling detailed of language use and facilitating computational processing. These annotations are typically applied to corpora—large collections of text or speech—allowing researchers to study patterns in , morphology, and meaning across languages. Early efforts in linguistic annotation date back to the , when manual tagging of small corpora was used to explore structuralist theories of , though this approach waned mid-century due to the rise of generative before reviving in the computational with digitized resources. Key types of linguistic annotations include part-of-speech (POS) tagging, which assigns grammatical categories such as , , or to words, and dependency parsing, which maps syntactic relationships between words in a sentence, often represented as directed trees. The Universal Dependencies (UD) framework, introduced in 2014 and formalized in in 2016, provides a cross-linguistically consistent scheme for dependency annotations, covering 186 languages as of November 2025 through harmonized treebanks that standardize POS tags, morphological features, and dependency relations. POS tagging schemes, like those in the Penn Treebank developed in the early 1990s, use tagsets such as the 36-tag scheme to annotate syntactic brackets and predicate-argument structures in English corpora exceeding 4.5 million words. Annotation for phonetics often employs schemes like the ToBI system for prosodic features in speech, while semantic annotations, such as those in PropBank, label predicate senses and argument roles to disambiguate word meanings in context. Prominent tools and resources include treebanks like the Penn Treebank, which serves as a benchmark for parsing algorithms, and the UD collection, which supports multilingual syntactic analysis. These resources are crucial for applications in (NLP) research, where annotated corpora train models for tasks like and . In , aligned parallel corpora—such as those in the UD framework or Europarl—provide sentence-level annotations linking source and target languages, enabling statistical and neural models to learn alignments for improved translation accuracy. Standards for linguistic annotations emphasize inter-annotator reliability to ensure consistency, often measured using , which accounts for chance agreement in categorical labels, as detailed in seminal surveys on annotation practices. Guidelines from projects like UD include detailed protocols for resolving ambiguities, with evolution from fully manual processes in the 1950s and 1990s treebanks to semi-automated methods today, where initial machine predictions are human-corrected to achieve agreement rates above 90% in controlled settings. Challenges in linguistic annotations persist, particularly with ambiguity in polysemous words, where a single term like "" can denote a or river edge, requiring context-dependent sense annotations that reduce inter-annotator agreement to around 70-80% without clear guidelines. Additionally, pre-2020s corpora often exhibited cultural biases, being predominantly Eurocentric and English-focused, which skewed representations of non-Western languages and dialects, limiting generalizability in global NLP applications until efforts like UD expanded to diverse languages.

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